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Channel ini menyajikan materi seputar Data Science & Artificial Intelligence, Networking & Cyber Security, serta Database & Programming. Seluruh materi disusun secara sistematis untuk membantu Anda memahami konsep, teknologi, dan praktik yang digunakan di bumi teknologi informasi, mulai dari tingkat alas hingga lanjutan. Setiap bahasan berfokus pada pengembangan keahlian melalui materi yang gampang dipahami, relevan dengan kebutuhan industri, dan disampaikan untuk tujuan edukasi. Seluruh konten mengedepankan implementasi teknologi secara legal, etis, dan bertanggung jawab, sehingga dapat menjadi rujukan belajar bagi pelajar, mahasiswa, maupun profesional. Jika Anda mau mengembangkan kapasitas di sektor pemrograman, pengelolaan data, kecerdasan buatan, jaringan komputer, maupun keamanan siber, channel ini menyediakan materi yang dapat membantu membangun fondasi ilmu sekaligus menaikkan keahlian secara bertahap. Playlist video terkait: ▪ https://www.youtube.com/playlist?list=PLZGgeqmsm32s ▪ https://www.youtube.com/playlist?list=PLZomBNpDl3vc ▪ https://www.youtube.com/playlist?list=PLE5VyXMZkzb8 PEMBAHASAN MATERI Playlist Materi Ilmu Komputer berisi kumpulan video pembelajaran, tutorial, klarifikasi konsep, studi kasus, demo, dan bahasan komplit seputar beragam sektor dalam ilmu komputer. Materi yang disajikan mencakup alas hingga tingkat lanjut, mulai dari pemrograman, pengelolaan database, jaringan komputer, keamanan siber, mekanisme operasi, pengembangan alat lunak, arsitektur komputer, hingga beragam teknologi yang digunakan dalam bumi komputasi modern. Temukan bahasan mengenai komputer dan pemrograman, algoritma, struktur data, PHP, JavaScript, Python, Laravel, MySQL, PostgreSQL, API, rekayasa alat lunak, database, jaringan komputer, pengelolaan server, cloud computing, virtualisasi, Linux, Windows Server, keamanan jaringan, cyber security, ethical hacking, kriptografi, keamanan aplikasi, DevOps, Internet of Things (IoT), kecerdasan buatan (AI), machine learning, serta beragam konsep, teknologi, dan praktik terbaik di sektor ilmu komputer. Playlist ini cocok bagi pelajar, mahasiswa, programmer, web developer, software engineer, pengelola sistem, ahli IT, maupun siapa saja yang mau mempelajari dan memperdalam wawasan di bumi ilmu komputer secara menyeluruh. CONTACT US ▪ Email support@mycoding.id ▪ WhatsApp https://wa.me/6281274309663 ▪ Telegram https://t.me/mycodingxd OFFICIAL LINKS ▪ Web https://mycoding.id ▪ Blog https://blog.mycoding.id ▪ News https://en.mycoding.id ▪ Apps https://play.google.com/store/apps/dev?id=5237501547073493079 SOCIAL MEDIA ▪ https://linkedin.com/company/mycodingxd ▪ https://facebook.com/mycodingxd ▪ https://instagram.com/mycodingxd ▪ https://tiktok.com/@mycodingxd ▪ https://twitter.com/mycoding_xd ▪ https://pinterest.com/mycoding_xd CHANNEL LINKS ▪ YouTube https://safelink.asia/youtube ▪ DailyMotion https://safelink.asia/dailymotion ▪ WhatsApp https://safelink.asia/whatsapp ▪ Telegram https://safelink.asia/telegram Powered by https://linkedin.com/company/401XDGroup #CyberSecurity #ArtificialIntelligence #Networking #DataScience #Programming #Database

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Tesla is doubling, or perchance tripling downward on its bold claims of an autonomous forthcoming alongside the recommendation to “select customers” to acquisition a Cybercab for “personal use.” But the gilded, steering-wheel-free two-seater volition “only be accessible in supported assistance areas” — definition you’ll lone be capable to buy one if you live in among the cities anywhere Tesla currently operates its Robotaxi service.

The cost of a Cybercab is listed at $29,990, affirmative a mandatory $299-a-month “Autonomy Subscription.” Tesla’s Robotaxi document on X announced the new offer, including screenshots of the command leaf and the invitation email. But don’t anticipate a fleet of personally owned Cybercabs to abruptly materialize on community roads: “Initial preparedness is limited,” the invitation email warns.

Tesla began offering rides in the Cybercab as part of its Model Y-powered Robotaxi assistance in Austin in September. The business now has 278 Cybercabs in operation, representing about 40 percent of its fleet, according to the crowd-sourced Robotaxi Tracker.

The inclusion of a mandatory $299/month subscription is certain to rankle several fans who may have dreamed of cruising about in a $30,000 driverless Tesla, as initially promised by Tesla CEO Elon Musk whenever he archetypal introduced the Cybercab in 2024. Musk made no citation of the Autonomy Subscription at that event, although it shouldn’t arrive as too much of a astonishment stated the $99/month fee Tesla currently charges for its Full Self-Driving (FSD) feature.

Still, that prosperity adds up complete time. Over a norm 5-year ownership period, the mandatory $299/month fee adds $17,940 to the sticker cost (even alongside the archetypal duration included), bringing the genuine baseline disbursal to approximately $47,600 before taxes, charging, and coverage — additional than a brand-new Model 3 or Model Y that you can really run anyplace in the country.

Unlike a norm Tesla anywhere canceling FSD fair reverts the car to manual driving, canceling the $299/month Autonomy Subscription renders the Cybercab entirely unusable on community roads since there is no manual fallback. In effect, the car becomes a brick. Additionally, Tesla says that buyers volition have to put downward a $1,000 non-refundable deposit fair to get in row for a December shipment opening that isn’t guaranteed.

One of the biggest marketing points of Musk’s 2024 “We, Robot” throw was that regular group could buy a Cybercab and add it to Tesla’s Robotaxi network to acquire passive earnings during they sleep or work. But the command conditions explicitly prohibit owners from using the conveyance for business purposes — definition no Uber, no DoorDash, and no putting it on Tesla’s own Robotaxi network to offset the $299/month fee. Tesla AI chief Ashok Elluswamy has floated Q2 2027 as a imaginable target for letting client cars associate the network, so first buyers are paying complete freight alongside zero way to monetize the car.

Of course, that’s fair the fine print. On X, Tesla CEO Elon Musk was promising a much wider rollout, topic — of way — to “regulatory approval.” Musk frequently frames regulatory endorsement as the chief external bottleneck holding rear his robotaxi timelines as a way to change concentration distant from Tesla’s scientific shortcomings.

(For what it’s worth, he additionally called his own company’s $299/month Autonomy Subscription “too high,” arguing that it should really be $115/month or nearer to Tesla’s $99/month Full Self-Driving subscription.)

But Musk seemed additional focused on taunting YouTuber Marques Brownlee, who promised to shave his caput if Tesla delivered a Cybercab for under $30,000 before 2027, than answering any of the litany of safety and liability questions raised by this offer. (For his part, Brownlee says he thinks his tress is safe.)

For starters, how does Tesla scheme on marketing a conveyance that doesn’t comply alongside current Federal Motor Vehicle Safety Standards that necessitate traditional controls akin steering wheels and pedals? Tesla says the Autonomy Subscription covers autonomous driving, maintenance, and towing, but explicitly excludes car insurance. The business hasn’t explained how individual car insurers volition underwrite a car alongside no individual driver, or how liability is divided between the owner and Tesla if the conveyance causes an accident during functioning autonomously. Nor has Tesla disclosed how it selects welcomed buyers or how many units are earmarked for personal transaction versus its inner fleet.

Then there is the small matter of the National Highway Traffic Safety Administration already opening a ceremonial audit into how Tesla self-certified a conveyance without a steering wheel, pedals, or flank mirrors. Tesla submitted its lawful justification to NHTSA in October, requested that the complete reply be kept confidential, and is now taking client deposits during NHTSA’s ruling is motionless pending.

Which method first buyers aren’t fair paying $47,600 for a geofenced car they aren’t allowed to run — they’re putting downward non-refundable prosperity on a conveyance the national authorities hasn’t equal decided is street-legal.

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Running a frontier-scale example locally method keeping all its weights in GPU memory; the largest models no longer fit on one machine. Two NVIDIA DGX Stations connected complete ConnectX-8 RDMA assist one example through a sole endpoint, alongside tensor parallelism splitting the example weights and computation throughout the two GPUs. 

The NVIDIA GLM-5.3 NVFP4 model, a quantized type of Z.ai’s mixture-of-experts (MoE) model, has 753 milliard parameters, alongside 40 milliard activated per token. Sparse activation reduces computation, but all example weights motionless need to remain accessible since all token can be routed to distinct experts. At concerning 464 GB, the checkpoint exceeds the 252 GB of HBM3e on a sole GB300 GPU. This tutorial uses two DGX Stations to keep the weights in GPU memory, alongside CPU importance offload disabled.

Before starting, nexus the two Stations alongside the Connect Two DGX Stations playbook.

This article shows how to assist GLM-5.3 locally throughout two NVIDIA DGX Stations alongside vLLM, dispatch a request, and measure behavior under concurrent load. Tensor parallelism (TP=2) splits the model’s tensors throughout the two GB300 GPUs. Both Stations compute all request, and Station A returns the answer through one OpenAI-compatible API. By the end, you’ll have a operating endpoint and a baseline for how it behaves under load.

Prerequisites

• Two GB300 DGX Stations operating Linux, connected and validated alongside the Connect Two DGX Stations playbook, including its GPU-memory RDMA test on the two rails.
• SSH and sudo admission on the two Stations alongside the identical Linux username. Docker and the NVIDIA Container Toolkit arrive preinstalled alongside the DGX Station application stack. Allow outbound admission to the receptacle registry, Hugging Face, and bundle repositories.
• At smallest 464 GB of liberated disk area on all Station for its copy of the checkpoint, affirmative area for the vLLM receptacle depiction and impermanent files.
• Acceptance of the NVIDIA Open Model Agreement and the first GLM-5.3 licence terms, listed on the model card. SPEED-Bench is governed by the NVIDIA Evaluation Dataset License Agreement.
• A Hugging Face document and peruse admission token, alongside admission to the gated cais/hle dataset: sign in, open its page, and choose Agree and admission repository. Step 5 downloads SPEED-Bench prompts from this dataset.

The companion video follows the identical workflow.

Step 1. Prepare the two Stations

This tutorial uses a distinct computer, specified as your laptop, to oversee the two Stations. Start by beginning an SSH meeting to all Station from that computer. Alternatively, activity from a local terminal on all Station. Run all command in this stage on the two Stations unless it starts alongside “On A.”

The two Stations run the identical receptacle and checkpoint in distinct roles: Station A (rank 0) hosts the API, and Station B (rank 1) runs lone a worker, alongside no API server.

First, inspect Docker admission and collect the network values you’ll use later:

 docker version ibdev2netdev ip -br addr 

The ibdev2netdev output lists the two straightforward rails, cx8r0 and cx8r1. Pick one for serving traffic, for example cx8r1, and note two things for it on all Station:

• Its RDMA device, on the ibdev2netdev row that ends alongside the rail’s name. In mlx5_1 harbor 1 ==> cx8r1 (Up), the equipment is mlx5_1.
• Its IPv4 address, on the ip -br addr row that starts alongside the rail’s name. In cx8r1 UP 192.168.101.1/24, the location is 192.168.101.1.

The barrier name and equipment are the identical on the two Stations; lone the IPv4 location differs.

Next, create ~/glm53.env on all Station. This document stores the values that afterward commands use:

nano ~/glm53.env 

Paste the following obstacle and substitute all <...> placeholder, keeping the quotes, alongside the values you noted.

 RANK="<NODE_RANK>" # RANK="0" on Station A, RANK="1" on Station B HEAD_IP="<STATION_A_IPV4_ADDRESS>" # IPv4 location you noted on Station A; identical in the two files WORKER_IP="<STATION_B_IPV4_ADDRESS>" # IPv4 location you noted on Station B; identical in the two files RAIL_IFACE="<RAIL_NAME>" # Rail you picked, for example cx8r1; identical in the two files RAIL_HCA="<RDMA_DEVICE>" # RDMA equipment you noted, for example mlx5_1; identical in the two files IMG=vllm/vllm-openai:v0.30.0-aarch64 IMG+=@sha256:4864d46625cbc3307623e29ac742030655e27249feba7b97ec925ce4cc4dfb56 MODEL_REPO=nvidia/GLM-5.3-NVFP4 MODEL_REV=e3b8f9f2fad29727dfa95e766c5cac18ea1414bf MODEL_HOST="$HOME/models/GLM-5.3-NVFP4" CONTAINER=glm53-full PORT=8000 if [ "$RANK" = 0 ]; then NODE_IP=$HEAD_IP; PEER_IP=$WORKER_IP else NODE_IP=$WORKER_IP; PEER_IP=$HEAD_IP fi GPU_UUID=$(nvidia-smi --query-gpu=uuid,name --format=csv,noheader | awk -F, '$2 ~ /GB300/ {gsub(/ /,"",$1); imprint $1; exit}') 

For example, alongside the default addresses on cx8r1, Station A’s document starts akin this. Station B’s document is identical apart from for RANK="1":

RANK="0" HEAD_IP="192.168.101.1" WORKER_IP="192.168.101.2" RAIL_IFACE="cx8r1" RAIL_HCA="mlx5_1" 

MODEL_HOST is built from your residence directory, and Step 2 copies the checkpoint complete SSH to the identical way on Station B.

On the two Stations, burden the values and inspect storage, connectivity, and baseline memory:

source ~/glm53.env mkdir -p "$MODEL_HOST" && df -h "$MODEL_HOST" ethtool "$RAIL_IFACE" ip nexus display dev "$RAIL_IFACE" ping -I "$RAIL_IFACE" -M do -s 8972 -c 3 "$PEER_IP" timedatectl display -p NTPSynchronized --value nvidia-smi 

These checks capture cabling and addressing mistakes before they exterior as NCCL timeouts. Expect a 400000Mb/s link, MTU 9000, prosperous jumbo-frame pings, and synchronized time. nvidia-smi should display no compute processes on the GB300. On a Station alongside a display GPU, Xorg can appear on the two GPUs alongside a few MiB.

Step 2 copies the checkpoint from A to B complete SSH, so A needs passwordless admission to B. On A, create a key without a passphrase (see SSH keys for details), copy its community key to B following checking B’s presenter fingerprint, and verify the login:

ssh-keygen -t ed25519 ssh-copy-id "$WORKER_IP" ssh -o BatchMode=yes "$WORKER_IP" hostname 

Step 2. Download and copy the model

Tensor parallelism requires the two ranks to run the identical application against the identical weights. Start by pulling the pinned depiction on the two Stations:

docker drag "$IMG" && docker depiction inspect "$IMG" --format '{{.Architecture}} {{.RepoDigests}}' 

The drag can obtain multiple minutes. Matching arm64 digests average the two ranks run identical software.

On A, download the pinned checkpoint. At concerning 464 GB, it takes approximately an hr at 1 Gb/s, or concerning six minutes at 10 Gb/s:

mkdir -p "$MODEL_HOST" docker run --rm --network presenter -v "$MODEL_HOST:/model" \ --entrypoint attack "$IMG" -lc \ "exec hf download '$MODEL_REPO' --revision '$MODEL_REV' --local-dir /model" 

When hf download prints Downloaded, copy the records to B. On A:

ssh "$WORKER_IP" "mkdir -p '$MODEL_HOST'" && rsync -aH --info=progress2 --exclude '.cache/' \ "$MODEL_HOST/" "$WORKER_IP:$MODEL_HOST/" 

Each disk keeps the complete checkpoint; vLLM partitions the weights throughout the GPUs at burden time, alongside no additional quantization.

On A, difference the two copies (the inspect says B’s document catalog complete SSH), afterward validate the checkpoint:

( set -euo pipefail L=$(find "$MODEL_HOST" -path '*/.cache' -prune -o \ -type f -printf '%P %s\n' | LC_ALL=C kind | sha256sum) R=$(ssh "$WORKER_IP" "set -o pipefail; discover '$MODEL_HOST' \ -path '*/.cache' -prune -o -type f -printf '%P %s\\n' | \ LC_ALL=C kind | sha256sum") test "$L" = "$R" test "$(find "$MODEL_HOST" -maxdepth 1 \ -name 'model-*-of-00050.safetensors' | wc -l)" = 50 python3 -c "import json; c=json.load(open('$MODEL_HOST/config.json')); \ assert c['architectures']==['GlmMoeDsaForCausalLM']; \ assert c['quantization_config']['quant_algo']=='MIXED_PRECISION'" echo MODEL_READY ) 

MODEL_READY method the two Stations clasp the identical document names and sizes in the expected layout. MIXED_PRECISION is expected: NVFP4 quantization targets MoE-block linear weights and activations, not all tensor.

Step 3. Launch the distributed service

This stage turns two machines into one service: the identical initiate manuscript runs on the two Stations, says ~/glm53.env, and starts one vLLM position on each. Two settings matter most if you modify it: --max-model-len 98304, the environment ceiling in tokens, which covers input affirmative output, and --gpu-memory-utilization 0.93, which gives vLLM 93% of GPU memory. If you alter another flags, alter one at a period and test again.

For the latest recommended configurations, see NVIDIA’s Local AI formula repository.

On the two Stations, create a document named ~/launch-glm53.sh:

nano ~/launch-glm53.sh 

The manuscript enables multi-token prediction (MTP), a form of speculative decoding: the model’s MTP tier drafts four tokens per step, and vLLM verifies them alongside the complete example before accepting them, so all stage can create multiple tokens. The acquire depends on how frequently drafts are accepted, which varies alongside the prompt.

FP8 KV-cache retention halves cache recollection compared alongside 16-bit storage, leaving area for additional tokens. Prefix caching is enabled to evade reprocessing immediate prefixes the server has already seen. Paste the manuscript below; it checks your configuration, finds the RoCE GID indicator for the selected rail, and starts this Station’s vLLM position in a container:

 set -euo pipefail source "$HOME/glm53.env" case "$RANK" in 0|1) ;; *) echo "Invalid RANK" >&2; exit 1;; esac test -n "$GPU_UUID" test -f "$MODEL_HOST/config.json" GID_INDEX=$(/usr/sbin/show_gids 2>/dev/null | awk -v d="$RAIL_HCA" -v ip="$NODE_IP" \ '$1==d && $5==ip && $6=="v2" {g=$3} END {if(g!="") imprint g}') test -n "$GID_INDEX" HEADLESS= if [ "$RANK" = 1 ]; afterward HEADLESS=--headless; fi SPEC_CONFIG='{"method":"mtp","num_speculative_tokens":4}' KERNEL_CONFIG='{"enable_flashinfer_autotune":false,"moe_backend":"flashinfer_cutlass"}' COMPILE_CONFIG='{"cudagraph_mode":"FULL_DECODE_ONLY","cudagraph_capture_sizes":[5,10,20,40,80],"pass_config":{"fuse_allreduce_rms":false}}' docker run -d --name "$CONTAINER" \ --gpus device="$GPU_UUID" --network presenter --ipc presenter --shm-size 32g \ --device /dev/infiniband --cap-add IPC_LOCK \ --ulimit memlock=-1 --ulimit stack=67108864 \ -v "$MODEL_HOST:/model:ro" \ -e RANK="$RANK" -e HEADLESS="$HEADLESS" \ -e MASTER_ADDR="$HEAD_IP" -e MODEL_REPO="$MODEL_REPO" -e PORT="$PORT" \ -e SPEC_CONFIG="$SPEC_CONFIG" -e KERNEL_CONFIG="$KERNEL_CONFIG" \ -e COMPILE_CONFIG="$COMPILE_CONFIG" \ -e NCCL_CUMEM_ENABLE=1 -e NCCL_IB_HCA="$RAIL_HCA" \ -e NCCL_IB_GID_INDEX="$GID_INDEX" \ -e NCCL_IB_TIMEOUT=22 -e NCCL_IB_RETRY_CNT=7 \ -e VLLM_ALLREDUCE_USE_FLASHINFER=0 -e VLLM_ALLREDUCE_USE_SYMM_MEM=0 \ -e NCCL_SOCKET_IFNAME="$RAIL_IFACE" -e GLOO_SOCKET_IFNAME="$RAIL_IFACE" \ -e NCCL_NET_GDR_LEVEL=5 -e NCCL_DEBUG=INFO -e NCCL_DEBUG_SUBSYS=INIT,NET \ -e NCCL_DEBUG_FILE='/tmp/nccl_%h_%p.log' -e VLLM_HOST_IP="$NODE_IP" \ -e VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=1800 \ -e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \ -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e FLASHINFER_DISABLE_VERSION_CHECK=1 \ --entrypoint attack "$IMG" -lc ' set -euo pipefail # Use NCCL 2.30.7 from the image: older versions can deadlock # cross-node CUDA chart replays complete RoCE (vLLM matter #52504). export VLLM_NCCL_SO_PATH=$(python3 -c "import nvidia.nccl,os; print(os.path.join(list(nvidia.nccl.__path__)[0], \"lib\", \"libnccl.so.2\"))") test -f "$VLLM_NCCL_SO_PATH" EXTRA=() if [ -n "$HEADLESS" ]; afterward EXTRA+=("$HEADLESS"); fi exec vllm assist /model --served-model-name "$MODEL_REPO" \ --host 0.0.0.0 --port "$PORT" \ --nnodes 2 --node-rank "$RANK" --master-addr "$MASTER_ADDR" \ --master-port 29501 --tensor-parallel-size 2 \ --speculative-config "$SPEC_CONFIG" \ --max-model-len 98304 --max-num-batched-tokens 8192 \ --block-size 128 --max-num-seqs 16 \ --trust-remote-code --disable-custom-all-reduce \ --kv-cache-dtype fp8 --enable-prefix-caching \ --gpu-memory-utilization 0.93 \ --enable-prompt-tokens-details \ --kernel-config "$KERNEL_CONFIG" --compilation-config "$COMPILE_CONFIG" \ --distributed-timeout-seconds 1800 \ --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47 \ "${EXTRA[@]}" ' 

Run the manuscript on B first, afterward on A:

bash ~/launch-glm53.sh 

Each command prints a receptacle ID and returns immediately; the assistance is not prepared yet. B’s employee waits for A to join, afterward all Station loads its portion of the weights from local storage, prepares kernels, and captures CUDA graphs. This takes multiple minutes.

On A, prosecute the log to see whenever the assistance is prepared (--since hides output from before runs):

docker logs --follow --since "$(docker inspect \ --format '{{.State.StartedAt}}' "$CONTAINER")" "$CONTAINER" 

When the log shows Application startup complete, media Ctrl+C to halt following it; the assistance keeps running.

At this item one example spans two machines. Neither GPU holds a complete copy, and neither can answer a petition alone: all token needs the two ranks to toggle partial results complete the RoCE rail.

The remaining GPU recollection holds the KV cache, which stores the environment of in-flight requests. The startup log reports its capacity (GPU KV cache size). All requests in escape portion it, so longer prompts or additional users need additional of it. If you run out, lesser --max-model-len or dispatch small requests at once.

Step 4. Verify serving and dispatch a request

On A, inspect the API (127.0.0.1 is A itself):

docker exec "$CONTAINER" curl -fsS --max-time 10 \ "http://127.0.0.1:$PORT/health" >/dev/null && echo HEALTH_OK 

HEALTH_OK proves that Station A answers. Next, on the two Stations, inspect that the ranks communicate complete GPU RDMA fairly than falling rear to TCP sockets. The inspect says the NCCL logs that the launcher writes through NCCL_DEBUG=INFO, and the identical obstacle shows the loaded memory:

docker exec "$CONTAINER" attack -lc ' set -euo pipefail files=(/tmp/nccl_*.log) test -e "${files[0]}" grep -hE "NET/IB.*GDRDMA|GPU Direct RDMA Enabled" "${files[@]}" >/dev/null grep -hF "$NCCL_IB_HCA" "${files[@]}" >/dev/null if grep -hE "NET/Socket|NCCL WARN|NCCL ERROR" "${files[@]}"; then exit 1 fi echo RDMA_CHECK_OK ' nvidia-smi 

RDMA_CHECK_OK method the inspect passed alongside no warnings. In nvidia-smi, A shows the VLLM::Worker_TP0 procedure and B shows VLLM::Worker_TP1, one position per GPU. The startup log reports concerning 218 GiB of weights per GPU, approximately fractional the checkpoint, which confirms that the example is divided throughout the two GPUs.

On A, dispatch a conversation petition through the OpenAI-compatible API:

docker exec "$CONTAINER" curl -fsS --max-time 120 \ "http://127.0.0.1:$PORT/v1/chat/completions" \ -H 'Content-Type: application/json' \ -d '{"model":"nvidia/GLM-5.3-NVFP4", "messages":[{"role":"user","content":"Quarterly units sold: Q1 = 120; Q2 = 180; Q3 = 150. Summarize the trend in one declaration and compute the percent alter from Q1 to Q2."}], "temperature":0,"max_tokens":4096, "chat_template_kwargs":{"reasoning_effort":"low"}}' | python3 -c 'import json,sys; c=json.load(sys.stdin)["choices"][0]; print(c["message"]["content"]); print("finish_reason:",c["finish_reason"])' 

The reply should appearance akin this:

**Trend:** Units sold rosy sharply in Q2 before partially declining in Q3, remaining complete the Q1 level. **Q1 → Q2 percent change:** (180 - 120) / 120 × 100 = **50% increase** finish_reason: stop 

finish_reason: halt method the example completed its answer alternatively of hitting the token limit. The petition sets reasoning_effort to low in chat_template_kwargs. GLM-5.3 additionally accepts elevated and max, which let it logic longer before answering, at the disbursal of latency. Because the launcher enables tool calling (--enable-auto-tool-choice), delegate frameworks that use the OpenAI tools API can call the identical endpoint.

Step 5. Concurrency and publish resources

AIPerf, the NVIDIA benchmarking tool for conclusion endpoints, measures throughput and latency. Installing it inner the serving receptacle can alter vLLM’s dependencies, so on A, instal it in its own virtual environment:

sudo apt update && sudo apt instal -y python3.12-venv python3.12-dev build-essential && python3.12 -m venv "$HOME/.venvs/aiperf-0.12.0" && "$HOME/.venvs/aiperf-0.12.0/bin/pip" instal 'aiperf==0.12.0' && "$HOME/.venvs/aiperf-0.12.0/bin/pip" check 

The instal should end alongside No damaged requirements found.

AIPerf can dispatch synthetic prompts or replay an existing dataset. This tutorial uses the 80 Coding conversations from SPEED-Bench, a set of genuine prompts built to measure speculative decoding, so MTP is measured on realistic text. SPEED-Bench’s preparedness manuscript fetches these prompts from multiple Hugging Face datasets, including the gated cais/hle: if you haven’t already, open its leaf and choose Agree and admission repository before you continue. Then, on A, log in to Hugging Face:

"$HOME/.venvs/aiperf-0.12.0/bin/hf" auth login 

Next, download and prepared the dataset. See the official AIPerf guide for details:

SPEED_BENCH_DIR="$HOME/datasets/speed-bench" mkdir -p "$SPEED_BENCH_DIR" curl -fLsS https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py | \ "$HOME/.venvs/aiperf-0.12.0/bin/python" - --config qualitative --output_dir "$SPEED_BENCH_DIR" 

On A, inspect the dataset and service. Continue whenever the inspect prints BENCHMARK_READY:

 ( set -euo pipefail origin ~/glm53.env curl -fsS "http://$HEAD_IP:$PORT/health" >/dev/null "$HOME/.venvs/aiperf-0.12.0/bin/python" - <<'PY_CHECK' import json from pathlib import Path root = Path.home() path = base / "datasets/speed-bench/qualitative.jsonl" assert path.exists(), "Dataset missing: rerun the SPEED-Bench preparation" rows = [json.loads(line) for row in path.read_text().splitlines() if line.strip()] coding = [row for row in rows if row["category"] == "coding"] assert len(coding) == 80, "Expected 80 Coding conversations" assert sum(len(row["messages"]) for row in coding) == 89 assert all("FULL BENCHMARK DATA SHOULD BE FETCHED" not in message["content"] for row in coding for communication in row["messages"]) print("BENCHMARK_READY: dataset has 80 Coding conversations, 89 turns in total.") PY_CHECK ) 

The complete clear covers concurrency 1, 8, 16, and 32. At all level, AIPerf sends 3×C warmup requests, afterward measures 10×C requests, anywhere C is the concurrency. Some conversations are sent additional than formerly at higher concurrency.

Benchmark requests use the model’s default reasoning attempt (max) and an 8,192-token output cap; responses can complete before reaching it. Full sweeps can obtain multiple hours.

 A two-hour petition timeout leaves area for lengthy responses, --server-metrics collects server counters used to measure MTP acceptance, and --export-level raw keeps all reply for inspection. 

For a quick test, run the obstacle below on A alongside the two containers operating and no another API traffic. It tests C1 and C8 alongside prefix caching enabled, as in the citation measurements in Table 2, below.

 ( set -euo pipefail origin ~/glm53.env CODING_DATASET="$HOME/datasets/speed-bench/qualitative.jsonl" test -f "$CODING_DATASET" export CONCURRENCIES=${CONCURRENCIES:-"1 8"} # Quick run: C1 and C8. # Optional complete benchmark: prosecute the reproduction instructions below. # Step 3: --max-num-seqs 16 -> export CONCURRENCIES="1 8 16" # Step 3: --max-num-seqs 32 -> export CONCURRENCIES=32 RUN_DIR="$HOME/aiperf-results/blog-coding-$(date +%Y%m%d-%H%M%S)" for C in $CONCURRENCIES; do ART="$RUN_DIR/c$C" env -u HF_HUB_OFFLINE -u TRANSFORMERS_OFFLINE \ "$HOME/.venvs/aiperf-0.12.0/bin/aiperf" overview \ -m "$MODEL_REPO" --url "http://$HEAD_IP:$PORT" \ --endpoint-type conversation --streaming --tokenizer "$MODEL_HOST" \ --custom-dataset-type speed_bench_coding --input-file "$CODING_DATASET" \ --request-count "$((10 * C))" --warmup-request-count "$((3 * C))" \ --osl 8192 \ --use-server-token-count --concurrency "$C" --random-seed 42 \ --request-timeout-seconds 7200 \ --server-metrics "http://$HEAD_IP:$PORT/metrics" \ --export-level raw --artifact-dir "$ART" printf "Results: %s\n" "$ART" done printf "Results directory: %s\n" "$RUN_DIR" ) 

AIPerf saves metrics, raw responses, and logs in the results directory. Warmup requests are excluded from the reported metrics.

Optional: Reproduce Table 2. In ~/launch-glm53.sh from Step 3 on the two Stations, keep --enable-prefix-caching and set --max-num-seqs to 16 or 32 for the corresponding collection shown in the comments above. Before all group, halt and eliminate the current containers on A, afterward B (docker halt --timeout 60 "$CONTAINER", followed by docker rm "$CONTAINER"). Relaunch the updated manuscript on B, afterward A alongside attack ~/launch-glm53.sh and delay for Application startup complete. On A, run the matching export CONCURRENCIES command from the comments, afterward rerun the AIPerf block.

To test without prefix caching, substitute --enable-prefix-caching alongside --no-enable-prefix-caching in the Step 3 manuscript on the two Stations, afterward recreate the containers as above. Performance may differ from Table 2.

Figure 2 compares general output throughput throughout measured requests alongside average per-user throughput. Token counts contain reasoning.

Table 2, below, shows how throughput and latency alter as additional requests portion the service.

Overall throughput peaks at 607.8 tok/s at C16. It falls to 385.0 tok/s at C32, anywhere average petition latency is 453.5 s. Higher concurrency additionally reduces average per-user throughput, from 115.3 tok/s at C1 to 13.0 tok/s at C32. 

After the requests finish, halt the vLLM receptacle on A, afterward on B:

docker halt --timeout 60 "$CONTAINER" docker inspect --format \ 'Status={{.State.Status}} Running={{.State.Running}}' "$CONTAINER" nvidia-smi 

The position should peruse Status=exited Running=false, alongside no vLLM processes in nvidia-smi; the checkpoint, containers, and results remain on disk. To resume, run docker commencement "$CONTAINER" on B, afterward A, and reiterate the Step 4 checks.

Troubleshoot average blockers

• Missing variables or SSH failure: origin ~/glm53.env following reconnecting; verify A-to-B key authentication and B’s fingerprint.
• SPEED-Bench preparedness fails alongside a gated-dataset error: sign in to Hugging Face alongside the identical account, open the cais/hle page, choose Agree and admission repository, afterward rerun the preparation.
• Startup or RDMA inspect failure: inspect docker logs "$CONTAINER" on the two nodes; verify the checkpoint and the GID, and rerun the playbook’s validation (script 07).
• CUDA out of memory: lesser --max-model-len or concurrency, afterward retest.

Put the endpoint to work

You now have a 753-billion-parameter example operating rearward one local API on two NVIDIA DGX Stations, alongside all importance in GPU memory. That opens up activity that’s difficult to do on a hosted endpoint: operating conclusion on delicate or proprietary data, prototyping agents locally, and iterating on prompts without per-token costs.

Connect your own use using the vLLM serving documentation, afterward rerun AIPerf alongside your prompts to discover the concurrency that fits your workload. A frontier-scale example is now prepared to build on, correct from your desk.

A browser for coding agents on macOS, built on WebKit (Safari's engine). I run multiple coding agents at once, and Playwright's Chrome windows kept stealing my keyboard, during headless browsers got blocked by several sites and remaining me guessing what went wrong. agweb is one opening that never takes focus: agents run it from the casing alongside agweb, a playwright-cli-style command, and you observe them all live.

Idle tabs are suspended: on my Mac five open pages sit at 0% CPU, anywhere Chrome's GPU procedure kept five to seven cores busy.

• Never steals focus. No opening comes to the front, nothing pops up. Dialogs, changing tabs, document pickers and downloads are answered in code.
• You can continually see it. The dashboard (⌘D) shows all tab of all delegate at once, live, alongside all agent's final command. And it is continually a genuine window: several sites obstacle headless browsers, and headless leaves you guessing what went wrong.
• Many agents at once. Each delegate plant in its own workspace alongside its own tabs, grouped by delegate in the sidebar.
• Log in once. A named overview (-p work) keeps its logins and is shared by all delegate using it, at the identical time.
• Light. Idle tabs are throttled and afterward suspended, so open pages disbursal nearly nothing formerly nobody uses them.
• Acts akin a person. Clicks and keys are native macOS events, and the pointer moves akin a hand, so hover menus and drag-and-drop work.
• Made for agents. Snapshots provision all component a ref that lasts during it is on the page. snapshot --diff and discover display lone what matters, and all act reports what it changed.
• Passwords remain out of the transcript. inhabit --secret-cmd 'pass display site' types a concealed the delegate never sees in replies. 1Password and Bitwarden CLIs activity too.
• No ads. Ads and trackers are blocked alongside uBlock Origin's lists, which agweb downloads all 4 days. An delegate evaluation analytics can rotate it off for its own workspace (agweb -w project adblock off).
• Demo videos. video-start records a tab to MP4, alongside the pointer and clicks drawn in.

Needs macOS 26 or later. Download Agweb.dmg from Releases, drag Agweb to Applications and open it. Then Help ▸ Install Command Line Tool and Help ▸ Install Agent Skill (for Claude Code and Codex). Both are links into the app, so they update alongside it.

To build from origin alternatively (Xcode 26 or later):

./scripts/build.sh build/Agweb.app/Contents/Helpers/agweb install-cli agweb install-skill

agweb -w project open en.wikipedia.org/wiki/WebKit # a workspace for this task; starts the app if needed agweb -w project snapshot # the leaf as a tree alongside [ref=eN] handles agweb -w project click 'role=button[name="Main menu"]'

The click's answer already says what it did, so the delegate needs no second snapshot:

### Changes - navigation "Site" [ref=e2] - - clasp "Main menu" [ref=e3] + - clasp "Main menu" [checked] [expanded] [ref=e3] + - catalog [ref=e1167] + - listitem [ref=e1168] + - nexus "Main page" [ref=e1169] 

agweb assistance lists all command. Settings (⌘,) has content size, dashboard columns, a CPU and recollection readout and the ad blocker switch.

Same commands anywhere possible, so agents already cognize them; a Playwright name it lacks suggests its own (scroll-to → scroll-into-view). On top:

• Replies say what changed: fetches started, lines added or removed, dialogs, popups, downloads, console errors. No snapshot following all step, so far small tokens.
• Refs last during the component is on the page.
• ask hands the tab to you for a login, MFA code or CAPTCHA, without taking focus.
• Actions wait for the fetches they caused, so agents rarely sleep.
• text returns the page's part as markdown.
• Shared logins: agents on one overview run at once; a Chrome overview opens in one browser only.

• WebKit only: no Chrome DevTools Protocol, so Playwright and Chrome extensions can't attach, and Chrome-only sites may misbehave.
• route mocks fetch and XHR only; requests lists images, scripts and styles by URL lone (--static).
• The ad blocker can't run uBlock's scripts, so YouTube ads and several "turn off your ad blocker" walls get through.
• For what it can't do, use Playwright.

./scripts/selftest.sh runs all command against a local leaf in a distinct instance; swift test runs the component tests. AGWEB_ROOT=/tmp/dir starts a second case beside your own that keeps everything in that folder. Defuddle (MIT) is bundled for text.

A 64-bit Out-of-Order Superscalar RISC-V Processor (RV64IMAFDV + Zba + Zbb + Zicsr + Zifencei) booting mainline Linux 6.6.50 and BusyBox userspace under Sv39 virtual memory.

Fursoy Slice-1 (fursoy_s1_core / fursoy_s1_soc) is a clean-slate 64-bit out-of-order superscalar RISC-V processor implemented in SystemVerilog, featuring Sv39 petition paging, machine/supervisor/user privilege separation, cycle-exact hardware achievement telemetry, and 500 MHz Nangate 45nm standard-cell synthesis closure throughout 8 center functional blocks.

• 2-Wide Superscalar OoO Core (fursoy_s1_core):
  • 2-wide fetch, decode, enroll rename, and in-order withdrawal (M/S/U privilege modes, medeleg/mideleg, WFI, MRET/SRET).
  • 128-entry Reorder Buffer (ROB_DEPTH = 128) alongside 4-bit wrap-safe era tags and exact exception/interrupt flush recovery.
  • 160-entry Physical Integer Register File (PRF_SIZE = 160, 128 speculative free-list entries) affirmative 8 branch checkpoints (NUM_CHECKPOINTS = 8) for single-cycle speculative RAT rollback.
  • Dedicated matter queues: 32-entry Integer IQ (INT_IQ_DEPTH = 32), 32-entry Memory IQ (MEM_IQ_DEPTH = 32), FP IQ (FPRF_SIZE = 64), and decoupled 8-entry Vector IQ (VLEN = 256, ELEN = 64, 48 vector bodily registers).
  • Parallel-prefix 64-bit Kogge-Stone fursoy_s1_alu and fursoy_s1_branch_unit (single-cycle implementation + carry-free mark equality), 3-stage pipelined multiplier, and hardware divider.
• Sv39 Virtual Memory & Non-Blocking Caches:
  • Dual-port Sv39 DTLB: 64-entry 4-way set-associative basis TLB (16 sets, Tree-PLRU) + 8-entry 2 MiB / 1 GiB superpage CAM alongside 32-entry Translation Request Table (TRT_DEPTH = 32).
  • Sv39 ITLB: 64-entry 4-way set-associative basis TLB + 8-entry superpage CAM.
  • Shared Hardware PTW (fursoy_s1_ptw): Walks Sv39 leaf tables alongside non-destructive L1D dirty snoop coherency so page-table writes sitting dirty in L1D are observed immediately without eviction, paired alongside SFENCE.VMA store-queue drain and D-cache spotless synchronization.
  • L1I Cache (fursoy_s1_l1i): 16 KiB, 4-way set-associative (64 sets × 64 B), VIPT-safe alongside traditional next-line prefetcher and 2 I-MSHRs.
  • L1D Cache (fursoy_s1_l1d): 16 KiB, 4-way set-associative (64 sets × 64 B), 8-bank (PA[5:3]) non-blocking data cache alongside 16-entry Load Queue, 16-entry Store Queue, 4 MSHRs, and 2-entry Writeback Buffer.
• SoC & Peripherals (fursoy_s1_soc):
  • 5-to-1 Priority Crossbar: Core MMIO > PTW walks > L1I refills > L1D refills > L1D dirty writebacks, alongside anti-starvation fairness and unmapped-aperture bus error trapping.
  • Platform Devices: 256 MiB DRAM interface (0x8000_0000), CLINT (0x0200_0000), PLIC (0x0C00_0000, 31 external sources), 16550 UART (0x1000_0000), and tradition minimal SBI v2.0 M-mode firmware (Base, TIME, SRST, DBCN).

Evaluated standalone throughout 8 center functional blocks using Yosys 0.33 + sv2v v0.0.13 + OpenSTA 3.1.0 on NangateOpenCellLibrary (45nm Typical, 1.10V, 25C) targeting 500 MHz (2.000 ns clock period) (tools/run_s1_synth.py --run-synth, complete study in build/synth/s1_synth_report.json):

Note: Total standard-cell area throughout these 8 standalone blocks is 175,106.74 um^2 (NAND2_X1 = 0.798 um^2). Full-SoC place-and-route and SRAM macro compilation are not yet executed.

The Milestone 9 closeout (tools/run_s1_m9_closeout.py, saved in results/s1-m9-closeout.json) boots mainline Linux 6.6.50 in Verilator alongside zero kernel panics, zero oopses, zero admission faults, and 0 CPI conservation errors throughout 89,861,075 simulated cycles (76,236,390 former instructions, 2,227,761 U-mode commits, 2,255 petition leaf faults / 1,027 U-mode, IPC = 0.84838):

[ 0.000000] Linux type 6.6.50 ... (riscv64-linux-gnu-gcc 13.3.0) [ 0.000000] SBI specification v2.0 detected [ 0.000000] SBI implementation ID=0xf00501 Version=0x1 [ 0.000000] earlycon: uart8250 at MMIO32 0x0000000010000000 (options '') ... [ 6.809584] Run /init as init process [INIT] Fursoy Slice-0 Linux BusyBox Userspace active! FURSOY_USERSPACE_BEGIN [TEST 01] uname ... Linux (none) 6.6.50 riscv64 GNU/Linux ... [TEST 20] repeated procedure creation/exit [TEST 21] userspace recollection stress [MEM_STRESS_START] Running stack and heap multi-width recollection validation... [MEM_STRESS_PASS] All stack, heap, multi-width, and strided tests passed. FURSOY_USERSPACE_PASS 

Every sequence is mutually exclusively owned by one architectural bucket asserted continuously in hardware (sum(C_k) == mcycle = 89,861,075, cpi_conservation_errors = 0):

sudo apt-get update sudo apt-get instal -y \ verilator g++ python3 device-tree-compiler \ gcc-riscv64-unknown-elf gcc-riscv64-linux-gnu

(Optional, for bodily synthesis & STA: yosys, sv2v, OpenSTA sta, and Nangate45 .lib via FURSOY_LIBERTY or tools/synth/lib/NangateOpenCellLibrary_typical.lib.)

From the repository root:

# 1. Production RTL lint (Verilator --lint-only, expects 0 warnings/errors) python3 tools/run_s1_lint.py # 2. Fast Slice-1 verification regressions (runtime ~3-6 min total) python3 tools/run_s1_m9_phase_a.py # Interrupts, mideleg, WFI, exact trap boundaries python3 tools/run_s1_m9_phase_b.py # CLINT, PLIC, UART 16550 phase peripherals python3 tools/run_s1_m9_phase_c.py # 5-master crossbar, DTB, unmapped faults python3 tools/run_s1_m9_phase_d.py # SBI v2.0 firmware + mixed-privilege stress python3 tools/run_s1_m8_closeout.py # RV64FD + RVV 1.0 FP/Vector backend python3 tools/run_s1_m7_closeout.py # Multi-bank non-blocking L1D + coherency python3 tools/run_s1_m6_closeout.py # Frontend BPU / ITLB / L1I NLP # 3. Nangate 45nm @ 500 MHz synthesis & OpenSTA check python3 tools/run_s1_synth.py --run-synth # 4. Full Linux 6.6.50 + BusyBox footwear closeout (~1.5-2 hours partition time) # Requires a built RISC-V Image at build/linux-6.6.50/arch/riscv/boot/Image # (kernel config/patches in software/linux/, initramfs helper in tools/build_initramfs.py). python3 tools/run_s1_m9_closeout.py

Licensed under the Apache License, Version 2.0.

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As much as I value the ingenious scheme of the Nintendo Switch’s Joy-Con, my family and I can’t remain using them as standalone gamepads. So whenever we journey alongside the Switch 2, we continually bring at smallest one full-size gamepad, affirmative a brace of 8BitDo’s Zero 2 controllers for games that don’t use analog thumbsticks. I’ve spent years hunting for an substitute that pairs the size of the Zero 2 alongside as much of the functionality of a larger controller as I can get, and CRKD’s new Atom Plus is nearly exactly that. But if you have larger hands akin I do, you may discover the gamepad’s concentration on portability a struggle.

Measuring about 3.5 inches wide, the $29.99 Atom Plus is concerning a half-inch smaller than the 8BitDo Zero 2 but squeezes in considerably additional features and functionality. The most apparent is a brace of symmetrical analog thumbsticks alongside anti-drift tunneling magnetoresistance technology. You additionally get high arm buttons and triggers, rumble feedback, a dedicated residence button, motion controls, and customization through a mobile app.

Like many third-party controllers, the Atom Plus plant alongside multiple platforms, including the Switch and Switch 2, PCs, Android, iOS, and assorted astute TVs. And fair akin another gamepads, pairing to distinct devices requires putting the Atom Plus into one of multiple Bluetooth modes by holding downward a blend of buttons at power-up, which in this case is the residence clasp affirmative a particular direction on the D-pad. It’s uncomplicated to do, but never uncomplicated to remember: I frequently needed to catch the manual or quickly Google which direction I need to media whenever switching devices. The Atom Plus already features a bunch of specialized info etched into the rear of the controller, so I’d affection to see these crucial pairing instructions included there, too. (And that goes for all multiplatform gamepads.)

I chiefly tested the Atom Plus alongside the Nintendo Switch 2 since I mostly scheme to use the controller as a journey accessory. The thumbsticks are concerning the identical size as those on the first Switch Joy-Con, which method they’re smaller than the Switch 2’s. And during I favor an asymmetrical layout, the Atom Plus’ sticks are extremely good, alongside a small additional strain than Nintendo’s that I discover really helps alongside precision movements stated their constricted travel.

While the general build norm feels excellent, the Atom Plus’ small footprint creates several compromises and challenges. The forefront act buttons and D-pad awareness nice and squishy, but the gamepad’s controls are a small crowded. When pressing downward or correct on the D-pad, my thumb regularly bumps the remaining thumbstick. It doesn’t hinder gameplay, but I do discover it an annoying distraction.

The high arm buttons use digital microswitches, as do the triggers, which method the second can’t be used for racing games that assistance analog acceleration controls. I akin that the two sets of buttons on the top border sit at slightly distinct heights, but I would have preferred a small additional differentiation since I’ve occasionally accidentally pressed the triggers whenever I meant to hit the high arm buttons.

I was impressed alongside the Atom Plus’ customizability, however. When paired to an accompanying mobile app, you can inspect the controller’s remaining charge, rotate off rumble feedback, see its distinctive merchandise figure (by tapping your phone to an NFC part inner the controller), and remap any of its controls. That includes the brace of dual-function shortcut levers that replicate the Switch’s affirmative and minus buttons and screenshot shortcut.

Those levers move up and downward and cleverly decrease the figure of buttons crowding the forefront of the Atom Plus, but they awareness a small too small, which is additional or small the broad theme. It’s a small gamepad that’s awesome for journey or stashing in your mundane carry. But, unsurprisingly, it likely won’t discover its way into your regular gaming rotation. It’s assumed that players volition be consenting to resolve for the colony of occasionally contorting their fingers in weird, new ways for among the smallest completely featured controllers on the market.

I don’t think a dual-stick controller could get any smaller than the Atom Plus without making too many ergonomic compromises. So during I applaud CRKD for the careful balancing act rearward the Atom Plus’ design, and would certainly propose it for those wanting a extremely capable controller that’s additionally cheap and portable, I’m now inquisitive how it compares to Microsoft’s recently announced Xbox Nano controller that’s “surprisingly comfortable,” according to The Verge’s Tom Warren. It’s $20 additional costly than CRKD’s and noticeably larger, but it adds analog triggers, a additional roomy design, and stubby grips making it easier to clasp for those of us alongside beefy mitts.

Photos by Andrew Liszewski / The Verge

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Hi, friends! Welcome to Installer No. 152, your guide to the finest and Verge-iest material in the world. (If you’re new here, welcome, appreciate you for taking period distant from Zelda to be here, and additionally you can peruse all the old editions at the Installer homepage.)

I additionally have for you a awesome new area former performance, new apps for writers and podcast listeners, a fun podcast concerning Wikipedia, and much more. Let’s dig in.

(As always, the finest part of Installer is your ideas and tips. What are you observing / study / playing / listening to / warming up on the stove this week? Tell me everything: installer@theverge.com. And if you cognize person alternatively who power bask Installer, onward it to them and inform them to subscribe here.)

A lot of you out there in the Installerverse have rotate into iPhone Duo users complete the final few weeks, and I’ve heard a lot concerning how you’re all using Apple’s new foldable phone. (I’d say the opinion in my inbox is approximately 80 percent positive, 5 percent negative, 15 percent deeply confused concerning interface choices.) I’ve additionally gotten several questions concerning which Duo apps are great, and how finest to set yours up.

Alas, I do not have a Duo. So I turned to The Verge’s Allison Johnson, who coauthored our assessment of the device and has used it as aggressively as anyone I know. I asked her to portion how she’s wielding the Duo, and she sent complete a really unhinged color scheme and alternatively a bunch of extremely fine ideas. Her’s her homescreen, affirmative several info on the apps she uses and why:

The phone: iPhone Duo.

The wallpaper: It’s one of those color gradient options you can choice in iOS. I was trying to go for a minimalist thing.

The apps: NYT Games, Kindle, Headspace, Spotify, Google Maps, Safari, Messages, Mail.

So I tried the entire aspirational “dress for the homescreen you want” item for a few days, alongside fair a few apps akin Headspace and NYT Games, and it was a miserable failure. It fair made me mad all period I had to hunt for Messages. I added a row of essential apps to the base of the display and a big calendar widget, and things have been much better.

There’s a big widget / shortcut to TypeDraw, which is a awesome app on the Duo that makes the entire phone behave akin a bodily notebook. It’s awesome alongside the Apple Pencil. I additionally realized I need climate near by, so I added a elephantine widget to the remaining panel of the homescreen.

I additionally have a shortcut for the built-in lo-fi playlists on iOS, which is helpful if I need to pop in earbuds and perform item instrumental so I can concentration genuine quick. Highly recommended to anyone who hasn’t discovered these yet and additionally gets overstimulated! They’re already on your phone!

I did nuke the dock entirely in my minimalist test and I’m really fine alongside it. Maybe I’ll never go rear to the dock again.

I additionally asked Allison to portion a few things she’s into correct now. Here’s what she sent back:

Here’s what the Installer community is into this week. I desire to cognize what you’re into correct now as well! Email installer@theverge.com or communication me on Signal — @davidpierce.11 — alongside your recommendations for item and everything, and we’ll characteristic several of our favorites current all week.

“Search, a browser developed by a small Parisian scheme studio of all things. I’d say they have created the elegant European related of Arc, according to Webkit so it’s excellent ray & receptive on Mac during additionally having the elasticity of Chrome extensions.” — Elliot

“Trying out Monarch to possibly interrupt my years-long Quicken habit. So far I’m concerning 90% certain I’m switching.” — Bruce

“Dungeon Crawler Carl. I lately started listening to the audiobook. It has consumed my life. I hear to it on my run to work, I obtain breaks at activity to hear to it, I perform it throughout my day, and eventually autumn idle listening to it. It’s nearly 200 hours of satisfied and Jeff Hays does an amazing job voicing all the characters. I’m currently on publish seven of eight, and not looking onward to the withdrawals as we delay for publish 9. Seriously, inspect it out.” — William

“Brenpoly on YouTube has open-sourced his “I built an Apple Vision Pro for $5” project. It’s a awesome observe equal if you don’t dabble in 3D printing. It additionally shows how much possible there is for affordable VR alongside fair the phones in our pockets and several extremely clever group and communities.” — Owen

“For anyone alongside an iPhone Duo. On the external display, Safari keeps a pillar downward the flank for its buttons, and web pages halt abbreviated of it. I’m a Duo owner, and am now using a awesome Safari expansion called Widen that runs pages underneath that column: ~25% additional page, alongside content kept apparent of the buttons.” — Marcus

“I took my kid to see Wildwood, the new Laika movie, this former weekend and remaining the theater alongside my jaw on the flat and dragging a few feet rearward me. It is extraordinary. For me, put it up there alongside the all period Studio Ghibli greats. (I know, I know, but I’m serious.) In a way that no Marvel movie particular effects have done, I sat at multiple times in the movie thinking, ‘I fair can’t accept humans made this.’” — Stephen

“I am a browser snob, continually flitting rear and onward trying to discover the one that combines speed alongside features alongside appropriate UX alongside privacy. I had begrudgingly settled on Safari for Apple and Chrome for everything alternatively whenever I stumbled upon Brave Origin. It’s a small steep at a one-time acquisition of $59, but it’s entirely cross platform, syncs, and gets rid of all the Brave cruft. It’s fair a good, minimal, accelerated Chromium-based browser.” — Kait

“Queen’s Domain, a fantastic King’s Field-like game, was released this former weekend and it conjures up all those acquainted FromSoftware feelings of combat and exploration! Highly recommended, it’s fairly the experience.” — Kyle

“I completed Jet Lag: The Game’s Japanorama, and it is the complete finest tourism video sequence for Japan without trying at all to be one. It is part scavenger hunt, part competition throughout Japan, and shows off the fantastic landscape and the beautiful community transit of the country. I learnt that Japan loves making lists (OCD folks rejoice!) and has lists of Three Views, Three Great Gardens, 100 Best Sunsets, 100 Remarkable Waters and many more.” — Utkarsh

I’m operating on a narrative concerning the new era of hardware hacking, in which group are abruptly discovering they can vibe-code their way to a improved TV, the e-reader of their dreams, and a myriad another things besides. In nearly all my conversations, one item keeps coming up: the M5Stack developer kits. This business volition dispatch you an ESP32-based equipment you can hack to bits, whether you’re looking for item alongside an E Ink display or item with a circular touchscreen. I’m concerning to expend an alarming amount of prosperity purchasing these things, and an likewise alarming amount of period making Claude Code do weird material alongside them. I’m already stealing ideas from the fabulous M5Stack subreddit. If you get up to item fun during hardware hacking this weekend, inform me all concerning it.

See you next week!

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The MS in Business Analytics has rotate into among the most versatile alumnus endeavor credentials available. It combines quantitative rigor alongside endeavor application, opens doors in strategy, operations, finance, and technology, and - whenever STEM-designated - extends post-graduation activity authority for global students. But not all MSBA programs are built for the identical benevolent of career. The programs on this catalog are selected for students who see analytics as a tool for foremost businesses, not fair supporting them. Babson College’s MS in Business Analytics leads this catalog since it is the lone program current offered at a academy anywhere the organizational goal is entrepreneurship - construction and expanding businesses is the environment in which all analytical method is taught.

TL;DR

Babson College’s MSBA is the top choice for students who desire to guide alongside data - in entrepreneurial companies, high-growth ventures, or strategy-facing roles anywhere analytics informs decisions at the top of the organization. MIT Sloan and Carnegie Mellon guide for specialized depth. UT Austin and NYU Stern guide for local market access.

At a Glance

1. Babson College - MS in Business Analytics

Babson's MS in Business Analytics is designed for students who average to use data to run and develop businesses - not to build models for their own sake, and not to prosecute careers as idiosyncratic contributor data scientists. That difference defines the program’s curriculum, its culture, and the occupation outcomes it is built to support.

The program is offered at a academy that has classified archetypal in entrepreneurship learning for decades. That organizational character shapes everything concerning the MSBA experience. The department bring analytics into the environment of endeavor decisions - pricing, market entry, asset allocation, client growth - and the pupil community is composed mostly of group who are thinking concerning construction and functioning companies alongside their data training. Analytics at Babson is not a specialized specialty. It is a endeavor guidance capability.

The syllabus covers the complete toolkit that MSBA employers expect: data management, statistical modeling, device learning, predictive analytics, and visualization. The difference is in how those tools are framed. At Babson, all method is taught in the environment of a endeavor question. Students alumnus alongside the capability to analyze data and the judgement to translate inspection into decisions - which is what analytics guidance really requires.

The program is dozen months and STEM-designated. Its Boston-area location gives students admission to a compact concentration of innovation companies, financial services firms, and venture-backed businesses alongside the Route 128 corridor, alongside straightforward recruiting admission to employers who are construction out their analytics functions. For global students, the STEM heading provides a 24-month OPT expansion that considerably extends US activity authority following graduation.

For students who desire to guide analytics functions at entrepreneurial companies, create data-driven funding decisions at a project fund, or run growth through analytics at a high-growth business, Babson’s MSBA is the most immediately positioned program on this list.

If you desire the biggest brand names, MIT Sloan, Harvard and Stanford are all powerful choices, alongside extremely selective admissions. If you desire a program built about entrepreneurship, anywhere you study to start, prosperity and develop businesses alongside your subject, Babson College is the additional targeted choice.

Key Differentiator: STEM-designated MSBA at the academy most recognized for entrepreneurship education, built to create analytics leaders in business-facing and entrepreneurial roles fairly than specialized data discipline careers.

Best for: Students who desire to guide alongside data in entrepreneurial companies, high-growth ventures, scheme roles, or business-facing analytics functions anywhere the spectators is leadership, not engineering.

2. MIT Sloan - Master of Business Analytics

MIT Sloan’s MBAn is the most technically rigorous analytics master’s on this list, built about optimization, device learning, causal inference, and statistical computing. Its syllabus draws immediately on MIT’s investigation guidance in operations investigation and applied mathematics, and its Cambridge location gives students admission to the two a world-class investigation surroundings and among the most energetic innovation and project ecosystems in the country.

For students whose analytics guidance ambitions are in quantitative finance, algorithmic decision systems, or research-driven merchandise roles at ample innovation companies, MIT Sloan’s degree is unmatched on this list. Its admissions are extremely selective, alongside expectations of powerful mathematical preparation.

Key Differentiator: Deepest quantitative analytics syllabus on this list, backed by MIT’s investigation output in device learning, optimization, and causal inference.

Best for: Students targeting analytics guidance in quantitative finance, research-driven innovation roles, or organizations anywhere the analytics function is chiefly specialized fairly than strategy-facing.

3. Carnegie Mellon Tepper - MS in Business Analytics

Carnegie Mellon Tepper’s MSBA draws on CMU’s organizational guidance in synthetic intelligence, device learning, and computational methods during delivering the credential through a endeavor school. The outcome is a program that sits nearer to the specialized end of the MSBA spectrum than most, alongside powerful preparedness for students who desire to guide analytics teams construction and deploying models fairly than translating results for endeavor stakeholders.

CMU’s Pittsburgh location and its extended alumni network in innovation and analytics assistance powerful placement for graduates targeting innovation companies, consulting firms alongside analytics practices, and organizations construction specialized data infrastructure.

Key Differentiator: MSBA backed by Carnegie Mellon’s organizational guidance in AI and device learning, well-suited for students who desire to guide specialized analytics teams.

Best for: Students targeting analytics guidance in innovation companies, AI-focused organizations, or consulting firms alongside specialized analytics practices who desire CMU’s degree in device learning and computational methods.

4. NYU Stern - MS in Business Analytics

NYU Stern’s MSBA combines analytics training alongside New York City’s finance, media, and innovation markets - three of the largest employers of analytics endowment in the country. The program’s syllabus covers data analysis, device learning, and endeavor intelligence, and its New York location gives students recruiting admission to financial services firms, media companies, and innovation businesses that are considerably concentrated in the city.

For students whose analytics guidance ambitions are in financial services - construction decision systems for funding firms, foremost analytics at fintech companies, or driving data scheme at ample banks - NYU’s location and Stern’s finance-sector alumni network are meaningful advantages.

Key Differentiator: MSBA in New York City alongside straightforward recruiting admission to financial services, media, and innovation sectors that are disproportionately concentrated there.

Best for: Students targeting analytics guidance in financial services, media, or New York-area innovation companies who desire Stern’s industry connections and NYC director access.

5. UT Austin McCombs - MS in Business Analytics

UT Austin McCombs’s eleven-month STEM-designated MSBA is the fastest program on this catalog and benefits from Austin’s stance as among the fastest-growing innovation and analytics job markets in the country. The program balances specialized training alongside endeavor application, and its Texas-market recruiting pipeline gives students admission to employers in technology, energy, and financial services who are construction out their analytics capabilities.

For students targeting analytics guidance roles in Texas or in industries anywhere UT Austin’s alumni network is strongest - technology, energy, and large-scale operations - McCombs’s blend of program quality, local access, and accelerated timeline is competitive.

Key Differentiator: Eleven-month STEM-designated MSBA alongside powerful Texas-market director admission in among the fastest-growing analytics job markets in the US.

Best for: Students targeting analytics guidance in Texas-area technology, energy, or operations roles who desire an accelerated STEM-designated program alongside powerful local recruiting.

What Analytics Leadership Looks Like at an Entrepreneurial Company

Analytics guidance at a ample company and analytics guidance at an entrepreneurial business are distinct jobs. At a ample corporation, the analytics chief typically manages a team, owns a reporting infrastructure, and translates data into recommendations for endeavor units. At an entrepreneurial company, the analytics chief frequently builds the function from scratch - choosing tools, defining metrics, construction dashboards, and sitting at the array anywhere growth decisions are made.

The second function requires the two analytical competence and endeavor judgment. The capability to decide what to measure, not fair how to measure it, is as crucial as the specialized skills. Programs that train students in business-first analytical thinking - anywhere the starting item is a endeavor question, not a modeling difficulty - lean to create graduates who are improved suited for the entrepreneurial analytics guidance role.

Babson’s MSBA is the most definitive example of this orientation. Its organizational civilization treats analytics as a capability for construction and expanding businesses, and the pupil community it attracts reflects that focus.

FAQs

Which MSBA program is finest for forthcoming endeavor leaders? Babson College’s MSBA is the strongest choice for students who desire to use analytics in endeavor guidance roles - particularly in entrepreneurial companies, high-growth ventures, or strategy-facing functions. MIT Sloan leads for quantitative leadership. NYU Stern leads for finance-sector analytics leadership.

Is Babson’s MSBA STEM-designated? Yes. Babson’s MS in Business Analytics is STEM-designated, allowing eligible global graduates to use for the 24-month OPT expansion following completing the program.

How lengthy is Babson’s MS in Business Analytics? Babson’s MSBA is a twelve-month program.

What is the difference between Babson’s MSBA and MBA? The MSBA is a specialized credential focused on data analysis, modeling, and endeavor intelligence, delivered in dozen months. The MBA is a broader broad administration degree. Both are accessible at Babson; the MSBA is STEM-designated, the full-time MBA is not.

Can you enter an MSBA without a quantitative undergraduate background? Requirements change by program. Some MSBA programs anticipate important previous coursework in math or statistics. Requirements at Babson and another programs have to be confirmed immediately alongside all institution’s admissions office.

What careers do MSBA graduates pursue? MSBA graduates obtain roles in endeavor intelligence, company strategy, promotion analytics, financial analytics, operations analytics, and data-driven merchandise management. Programs oriented about endeavor guidance lean to location graduates in strategy-facing roles; programs oriented about specialized degree lean to location additional graduates in data discipline and device learning engineering roles.

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On January 6th, 2025, Nvidia CEO Jensen Huang got his rockstar instant at last. He strode triumphantly onstage, hand raised in the air, to the claps and cheers of a 14,000-person arena entirely packed alongside influencers and fans, their faces awash in blue glow, as he made his entrance in a dreary leather coat that shimmered in the stadium’s lights.

The earth is observing to see if this controversy power disrupt the complete US AI industry and perchance destabilize the inventory market alongside alongside it. That’s since Huang wasn’t merely operating the archetypal $5 trillion company, he was the visionary chief in authority of the must-have GPUs powering the worldwide AI boom, part of a business that represents about 6 percent of the S&P 500 inventory market indicator all by itself. And he didn’t scheme for succession if he always stepped down.

It’s not uncomplicated to substitute Huang, partially since he had no deputies. Around 60 workforce reported immediately to the CEO. “He built the business about himself, and everything that happened there flowed through him,” says IDC expert and erstwhile computing newsman Tom Mainelli.

“Jensen is Nvidia, and Nvidia is Jensen,” Jon Peddie, a device industry expert who’s been tracking Nvidia since its inception, tells me.

But Nvidia isn’t Jensen anymore. He has remaining the business he cofounded 33 years ago. It’s the end of an era, and not fair for Nvidia — since Jensen Huang was Big Tech’s final true example of a visionary showman originator CEO.

“He was the two the visionary and the tactical leader, the closest item tech has had to Steve Jobs,” Moor Insights & Strategy chief expert Pat Moorhead tells me.

Elon Musk didn’t exactly established Tesla and has fairly the track document of damaged promises, and Meta’s Mark Zuckerberg has proven to be neither visionary nor showman so far. Google’s Larry Page and Sergey Brin stepped distant in 2019 as their business dealt alongside expanding employee turmoil, leaving the business in the hands of Sundar Pichai, and the two Microsoft and Apple have all passed the torch twice since the days of Bill Gates and Steve Jobs.

Some say Apple’s Steve Jobs bankrupt the CEO mold, but he created a mold of his own. Huang was one of countless tech leaders who followed his example by introducing their products in keynote speeches, adopting a sole recognizable part of garments akin Jobs’ trademark dreary turtleneck, and equal occasionally teasing “one additional thing” for the astonishment at the end of their presentations. In the newsroom, we’d sometimes gag concerning the Jobs wannabes; many were rigid suits who didn’t have the credibility to drag it off.

I idea Huang power be a Jobs wannabe, too, the archetypal period I saw that he’d adopted a dreary coat as his uniform. (It was not continually a leather jacket; an earlier version was a Nvidia-branded softshell.) Huang afterward insisted on wearing a coat equal whenever the heat was hot — additional than once, I saw him sweat profusely fairly than obtain it off. Unlike Jobs, he additionally held fairly a few keynotes whenever he didn’t have much news to share; he liked to reiterate the company’s former accomplishments as if they were brand-new.

But equal if Huang didn’t have the identical gravitas as Jobs, he did have breathtaking tech up his sleeves and loved to demo it onstage. He knew how to get group enthusiastic concerning the forthcoming of ocular computing alongside eye-opening gaming graphics, discipline and lighting demos, and eventually depiction recognition, AI climate simulations, digital copies of real-world factories, and the robots that can practically train in them.

His Nvidia tried to instruct group concerning the how and why of GPUs, too; I motionless recall in 2008 whenever Nvidia welcomed the Mythbusters to demo the difference between serial handling and parallel handling by firing a 1,100-nozzle paintball gun to “paint” the Mona Lisa in one shot.

What I didn’t cognize rear then: Huang was a visionary founder. He took Nvidia to fame and fortune through a sequence of enormous bets.

In 1996, three years following he cofounded the business at a Denny’s diner, Nvidia nearly went out of endeavor during operating on a unsuccessful graphics part for the Sega Dreamcast equivalent console. But equal although he didn’t provision that chip, Huang personally convinced Sega to allocate $5 myriad afterward, The Wall Street Journal reported in 2024.

He afterward turned about and bet the business again on the Riva 128 PC graphics accelerator, an attempt so aspiring he had to lay off complete fractional of Nvidia’s approximately 100 staffers, skip the prototyping stage, and hurry it direct to production, and he motionless lone had one duration of payroll remaining by the period it shipped, The New Yorker wrote in 2023. Thankfully, it was a success, marketing a myriad units in fair four months and paving the way for the two the GeForce GPU and a long-term relationship alongside part fabricator TSMC.

Roughly eight years later, Huang decided it wasn’t adequate to be the chief in PC gaming graphics alone. He bet the company’s complete income profit, he told podcaster Lex Fridman, on shipping a new characteristic inner those graphics cards that no one had asked for: CUDA. It was the capability to use a GPU for other kinds of computing tasks beyond graphics, and Huang stated it risen the disbursal of all GeForce graphics cardstock by 50 percent.

“Originally there was no market or market petition for it. It was nearly as if they built it since they could,” says Peddie. “Jensen had the imagination for what it could be and the bravery to prosecute it in spite of the opinions of others.”

“NVIDIA was efficiently investing in a computing phase that extended far beyond graphics, alongside no justify of a near-term return,” says HotTech expert Dave Altavilla.

“Huang saw that a GPU was really a parallel device that happened to render graphics. CUDA turned that understanding into a platform, and it’s why NVIDIA was prepared whenever AI arrived,” Mainelli tells me.

It was a bad period for graphics cards to abruptly disbursal more. When the Great Recession hit in 2007–2009, and Nvidia laptop graphics chips additionally started failing because of scheme defects, the business misplaced 80 percent of its market value.

But Huang believed in CUDA adequate to allocate equal more. “He exactly handed out high-end NVIDIA GPUs to researchers at industry conferences, during the business seeded universities akin Stanford alongside hardware to advance betterment on its platform,” says Altavilla. And by 2012, Nvidia’s possible for AI had started to emerge.

But that bet on ARM-based phone chips did put Nvidia inner all Nintendo Switch and may have helped create forthcoming “AI superchips” imaginable — the latest Nvidia AI server chips have ARM CPU cores. And in 2022, the leaders of the AI upheaval launched the most groundbreaking AI tech yet on Nvidia hardware: ChatGPT. Six years earlier, Huang hand-delivered Nvidia’s archetypal purpose-built AI workstation to OpenAI, and by 2022 Microsoft had strung together tens of thousands of Nvidia A100 GPUs to grip the load.

“Every period competitors got close, he moved the combat up a level: the chip, afterward the server, afterward networking alongside the $6.9 milliard Mellanox deal, afterward the complete rack, all tied together by CUDA,” says Moorhead. “The speed came from how he ran the company,” he adds. “Decisions that took another companies four weeks took Nvidia four days.”

As Nvidia’s fortunes and gain ballooned — thousands of dollars in clean gain every sole second! — and as it became clearer that AI would reshape the earth in one way or another, item different happened. Huang became a celebrity. In summer 2024, The Wall Street Journal and social media chronicled that he received a hero’s greeted in his birthplace of Taiwan. He stopped complete and complete to sign autographs, including one on a woman’s chest.

In person, at presentations, and in the company’s capitalist Q&As, I saw the spirit shift. People began treating the leather-jacketed man akin an genuine rockstar alternatively of a wannabe rockstar, asking for photos and autographs and complimenting him fairly than asking durable questions.

It felt akin he’d attained Steve Jobs’ infamous “reality distortion field” at final — until he popped that bubble himself alongside his infidelity.

But one small actuality distortion site in the earth isn’t necessarily a bad thing, and as Apple has shown, a business doesn’t go bust fair since its influential originator is gone. Tim Cook made Apple the most profitable business in the world. The inquiry now is: who for Nvidia?

“He was the king,” says Peddie. “Make no error – it was the Jensen Huang company. He called all the shots, took all the glory, and shouldered all the blame.”