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The archetypal business merchandise that I worked on following going on depart of deficiency from Apple in March 1984 (see Leave Of Absence) was a low cost, elevated resolution scanner for the Macintosh called Thunderscan, that I created in collaboration alongside a small business named Thunderware (see Thunderscan). I started operating on it in June 1984, and by first October, it was nearly complete.

Tom Petrie, among the two principals at Thunderware (the another was Victor Bull, who I worked alongside on the Silentype thermal printer (see What Hath Woz Wrought), the archetypal project that I did for Apple), arranged a few demos for assorted device magazines, to curry favorable reviews to advance the product. On October 11th, 1984, I drove alongside Tom to an agency in Hillsborough, to display Thunderscan for Byte magazine.

The Byte reviewer was John Markoff, a innovation scribe for the San Francisco Chronicle who was among the finest reporters covering the individual device industry. Tom described Thunderscan during I set up the demo and started scanning. John asked a few questions, taking notes alongside his IBM PC, which was operating a character-based content publishing company that I viewed alongside the representative devout disdain of a Macintosh purist. As I was answering one of his questions, the phone rang.

"Excuse me," he told us, as he pressed a key blend on his keyboard, his detect display immediately changing to a distinct program. He talked on the phone for a infinitesimal or two, occasionally typing, before he completed the conversation and pressed a key blend to toggle rear to his Thunderscan notes.

"What did you fair do?," I asked John, inquisitive concerning the application that he was running. "How did you toggle to another use so quickly?"

"Oh, I'm operating Memory Shift. Haven't you seen it?" John responded. "It's a DOS usefulness program that keeps multiple applications inhabitant in memory, and allows you to toggle between them quickly. I've been using it a lot lately." John typed the toggle command a few times in fast succession, to display me how accelerated it could do its thing.

"You know, I think I could do that for the Macintosh", I abruptly blurted out, before I equal idea concerning it consciously.

The 512K Macintosh, alongside four times the recollection of the original, had fair started shipping a few weeks ago. I had considered trying to run multiple applications simultaneously on the 512K Mac, but I was stymied by low recollection conflicts and another possible gotchas. But now, as I observed Memory Shift in act on John's PC, I abruptly saw a uncomplicated way to do it, which didn't appear to be too difficult to implement.

"Yeah, that would be cool," John agreed. I continued alongside the Thunderscan demo, but it was difficult for me to concentration on it, since I couldn't halt thinking concerning use switching. There were a few intricate problems to solve, but it seemed eminently doable, and I idea it would be incredibly helpful if I got it to work.

Tom Petrie noticed how enthusiastic I was concerning the new idea and, as he drove me residence from the demo, he reminded me of my previous commitments, and made me commitment that I would complete the alpha publish of Thunderscan as shortly as possible, before daring to commencement item new. We accepted on a catalog of a dozen or so tasks, which I idea I could accomplish inside two weeks. Once the alpha publish was completed, I could obtain a abbreviated hiatus, in command to activity on use switching, before diving rear into Thunderscan to complete the 1.0 release, due by the end of November.

During the next two weeks, I focused on polishing Thunderscan to get it prepared for the alpha release, but I additionally spent idle moments pondering the scheme of the use switcher. One essential decision was whether or not to burden all of the applications into a sole heap, which would create optimal use of recollection by minimizing fragmentation, or to allocate distinct "heap zones" for all application. I decided to opt for distinct heap zones to improved isolate the applications, but I wasn't certain that was right.

There were many of small problems to solve. The most crucial one was that the scheme application kept many of application-specific earth variables in low recollection (see Mea Culpa), which needed to be exchanged during environment switching, so all use could keep its own set of them. The difficult part was coming up alongside the exact catalog of exactly what needed to be swapped; many of the variables were obvious, but several were fairly subtle, and reliant on how applications were using them. I knew that my archetypal cut wouldn't be perfect, but I was assured that I could debug the inevitable problems formerly I saw how the applications were really failing.

A few days following starting the shove to complete Thunderscan, I received an intriguing phone call from Jeff Harbers, the director of Microsoft's Macintosh applications team. Jeff told me that Microsoft had a extremely strategic project that they needed for the Macintosh, and that they idea that I was the ideal individual to execute it. He wouldn't inform me item alternatively complete the phone, but he offered to fly me up to Seattle to conversation it in person. Even although I was correct in the center of trying to complete Thunderscan, I was intrigued adequate to obtain his recommendation to fly up to Seattle and sojourn alongside him the following Tuesday.

Jeff picked me up at the airport, and we drove to Microsoft's chief construction anywhere we were joined by Neil Konzen, a gifted 23 twelvemonth old who was Microsoft's chief systems programmer on the Macintosh. I knew Neil from his days as an first Apple II hobbyist, whenever we collaborated on adding features to an gathering tongue betterment scheme whenever he was lone 16.

Jeff asked me what I was operating on, and I told him concerning Thunderscan, which he seemed to be curious in. But whenever I mentioned that I was concerning to commencement several experiments alongside an use switching utility, his jaw dropped, and he looked akin he couldn't accept what I said.

"That's fair what we wanted to conversation alongside you about!", he exclaimed, "It's awesome that you're already operating on it".

Jeff explained that Microsoft had put a lot of attempt into getting their applications to run fine in the small area accessible in the 128K Macintosh, which they considered to be a key rivalrous advantage. But as things stood, the 512K Mac would undermine their efforts, since it allowed applications to be much larger. Plus, Lotus had lately announced an unified use suite for the 512K Macintosh called Jazz that made it uncomplicated to quickly toggle between distinct functional areas. But if the Macintosh could run multiple applications simultaneously, the small recollection footprint of the Microsoft apps would continue to be advantageous, since their lesser recollection requirements meant that additional of them could run concurrently, and users could put together tailored use suites on their own. The intent of the sojourn was to convince me to compose an applications switcher under agreement to Microsoft.

Neil Konzen had contemplated a possible design, which he conveyed to me in forefront of a whiteboard. He decided to use the sole heap method that I had rejected, alongside alongside a few engaging twists to minimize recollection fragmentation. I told him concerning the substitute method of using distinct heapzones, and how I idea that it was likely value it to commerce several recollection fragmentation for greater robustness. I told him that I would provision his method several additional thought.

Finally, my midday at Microsoft culminated in a personal gathering alongside their CEO, Bill Gates. Jeff ushered me into Bill's agency and reviewed the afternoon's discussions for him, before excusing himself to depart us solitary to negotiate a betterment deal. I had met Bill a few times during the way of Macintosh development, and during I respected his specialized acumen, I was wary of his burgeoning position as a conniving businessman.

After exchanging a few pleasantries, and telling me how much the Macintosh mattered to Microsoft, he looked me in the eye and said, "You're a really fine programmer, right? I think you must be a really fine programmer."

"I conjecture so," I responded, not understanding why he was attempting to flatter me.

"Well, I think you are. How lengthy do you think it volition obtain to do this project? A duration or two? I think a really fine programmer akin you could get it done in small than two months."

"I really have no idea," I replied, "I'm not far adequate alongside to cognize if it's equal feasible yet."

"Well, let's fig it out," he stated in a slightly condescending tone. "I don't think it could be additional than 10,000 lines of code, and a really fine programmer akin you have to be capable to compose at smallest a thousand lines of code per week, so I think it volition obtain you small than 10 weeks to compose it, if you're as fine as I think you are."

I didn't cognize how to respond, so I kept quiescent and let him continue.

"And how much do you think a really fine programmer should get paid? Around current we pay our finest programmers about two thousand dollars per week. Do you think you have to be paid additional than that?"

"I don't know," I replied. I was eventually commencement to see anywhere he was coming from. Bill was trying to get me to brag that I could compose the use switcher really quickly, so he could validate paying me a lesser cost for it.

"Well, I don't think that you could anticipate to get additional than four thousand dollars per week, tops. Actually, I think that's too much, but let's go alongside that. If it takes ten weeks, and you get paid four thousand dollars per week, that method you should get paid $40,000 for penning it."

$40,000 didn't audio akin extremely much to me, particularly if it was as strategic to Microsoft as it seemed to be. I think Bill was expecting me to create a counteroffer, but I wasn't extremely enthusiastic concerning marketing it to Microsoft despite of compensation, since it really should eventually be part of the Mac OS.

"Listen, I really desire to compose this, entirely independently from you guys, so you won't have to pay me item to do it. I certainly don't desire to negotiate a agreement until I see how it turns out; there power be a show-stopper and I won't be capable to get it to activity at all. And if I drag it off, it really have to be bundled alongside all 512K Macintosh."

Bill shifted his tactics. "OK, I don't really attention if Microsoft owns it as lengthy as it's accessible to our users. I desire you to commit that you'll use your finest efforts to making certain that it runs fine alongside our applications, and that you'll call Jeff if you run into any snags. We can conversation again concerning publishing it afterward if you desire to following you're additional along. How does that sound?"

I told him that echoed good, and I promised to do my finest to create it activity awesome alongside Microsoft's applications, which I wanted to do anyway, since the Microsoft apps were crucial to most users. We shook hands and I departed on a affirmative note.

By the period I returned to my home in Palo Alto that evening, I was burning alongside the desire to see if I could get item going quickly. Even although motionless I had a few additional days of activity to complete the Thunderscan alpha release, I decided to see if I could compose a evidence of idea prototype of the use switcher first.

I would eventually have to compose a person interface for selecting applications, but the evidence of idea didn't have to concern concerning that; it was hardwired to run MacPaint, MacWrite, MacDraw and the Finder. I worked for 20 hours direct penning the center of the program, which worked by patching traps to broaden a few essential scheme calls, akin GetNextEvent, Launch and ExitToShell. The hardest part was going through all of the low recollection locations, determining what needed to be swapped. It was incredibly satisfying to see it commencement to work, crashing all the period at first, but gradually stabilizing as I tracked downward assorted problems.

I had it operating for an hr or so whenever I saw Bud Tribble come back home. Bud had eventually completed his M.D./Ph.D. program at the University of Washington, including interning for a year, but he decided that operating on the Macintosh was additional fun than being a doctor, so he had returned to Apple in his old job as application director a few months earlier, in July 1984. Bud was living at Burrell Smith's house, which was next entrance to mine.

I dragged Bud complete to my home to display him how I could quickly toggle between MacPaint, MacWrite and MacDraw. He was impressed, but to my astonishment he complained that the switching, which was nearly instantaneous, was really too fast.

"I think it power be confusing to toggle from one use to another without any feedback," he told me. "What if person switches accidentally? Maybe you could use animation to create a smoother transition."

That echoed akin a awesome idea to me. We decided that one use should scroll off the display horizontally during another was scrolling on, which gave the users a simple, tangible mental example of the applications wrapped about a kind of Lazy Susan, which they could rotate to move the desired use to the apparent area. I quickly wrote several accelerated scrolling routines, and was blown distant by how chill it looked to see the applications zip throughout the screen.

I started showing my evidence of idea demo to my allies at Apple, as fine as a few person collection demos, and it was extremely positively received. Unfortunately, I motionless had to complete up the merchandise publish of Thunderscan, which I barely managed to do by the end of November as I had promised. After taking a abbreviated vacation, I got rear to activity on what I was now calling "Switcher" in first December and by Christmas I had much of it implemented, including a uncomplicated UI for selecting applications and "Switcher Documents" for remembering sets of connected applications.

In first January 1985, I got a phone call from Guy Kawasaki, one of Apple's third gathering evangelists, who told me that Apple was curious in purchasing Switcher, and that he was assigned to create that happen. The archetypal stage was arranging a demo for Steve Jobs.

I entered Steve's agency alongside a bit of trepidation, since I idea that Switcher was value at smallest a fourth of a myriad dollars to Apple, but I was certain that Steve would never desire to pay me that much. But I was additionally arrogant of Switcher, and was curious in seeing how Steve would react to it.

I booted up my by now norm demo of MacWrite, MacPaint, MacDraw and the Finder, as fine as a small maze generating program written by Steve Capps. I configured Switcher alongside the scrolling animation initially turned off, so it would have additional effect whenever I showed it later. I demoed cutting and pasting between MacWrite, MacPaint and MacDraw, in seconds alternatively of minutes, and afterward I turned on the scrolling animation, and starting switching quickly between them, in the two directions.

"OK, I've seen enough, " Steve interrupted me. "It's great. Apple is going to bundle it alongside the Mac. Congratulations."

But afterward he paused, and stared at me for a instant alongside an incredibly serious gaze, as if he was sizing me up or perchance fair trying to scare me.

"But I don't desire you taking advantage of this situation. I'm not going to authorize you to obtain advantage of Apple."

"What do you mean?" I asked him, genuinely puzzled.

"There's no way that you could have written that program without confidential data that you learned by operating at Apple. You don't have the correct to accusation any you akin for it."

I started to get angry. "The program is lone fractional finished, and if I don't think you're paying me fairly, I won't be motivated to complete it."

Steve gave me another serious gaze as he paused for a few seconds. Then he stated a sole number, without explanation.

"One hundred thousand dollars."

"I don't know," I told him, "I think it's likely value a lot additional than that."

"Don't contend alongside me. $100,000 is fair, and you cognize it."

I didn't appear to have any substitute but to capitulate to Steve's cost setting, since he's difficult to contend alongside and I really wanted Switcher bundled alongside the Mac. I eventually negotiated the final accord alongside Guy Kawasaki, where, in supplement to the $100,000, I managed to get a 10% royalty of the wholesale cost if Apple sold Switcher separately, which Steve swore they would never do, but eventually the royalty delivered another $50,000.

Getting Switcher going wasn't that hard, but it was a extremely difficult program to finish, since it overturned several of the underlying assumptions that applications were making, yet it was committed to keeping everything operating anyway. Much of the activity in the second stages engaged evaluation it alongside all use that I could get my hands on and debugging crashes. Usually I could concoct several benevolent of method that would mitigate the issue without causing worse problems elsewhere.

Predictably, the hardest part of finishing Switcher was making it activity smoothly alongside the Microsoft applications. That was partially since Microsoft was a extremely first developer, and took liberties alongside the scheme that most developers would shy distant from, but it was mostly since the instructions comprising their applications were encoded in pseudo-code to preserve space, in the tradition of the byte-code interpreters from Xerox, which Charles Simonyi advocated.

Unfortunately, the pseudo-code kept me from disassembling the program whenever it crashed, which made it additional difficult to debug. I eventually developed a debugging method by sole strolling through their interpreter, six instructions at a period to get to the instructions that were doing the work, but it was beautiful painful. I was resolute to slog through it since of the commitment that I made whenever I visited Microsoft.

One of the final problems that I addressed before finishing the archetypal publish of Switcher in March 1985 had to do alongside applications hanging. If you're operating multiple applications, you don't desire one use hanging to obtain downward all of them. I added a characteristic that allowed the person to slay the current use if it was hung up, by monitoring for a particular key blend during the vertical blanking interrupt handler.

I knew that I had to choice a extremely rare key combination, since you didn't desire users slaying their applications accidentally. I decided on shift-command-option-period, four keys held downward at once, which I idea would be beautiful difficult to stumble into accidentally. But I was amazed whenever I got a call from Jeff Harbers at Microsoft.

"Hey, I akin that abort characteristic that you fair added, but you're going to have to alter the key combination, since we're using that in Microsoft Word.", Jeff told me. Microsoft Word was extremely complex, and it possessed an enormous range of keyboard shortcuts, way too many, as far as I was concerned.

"OK, propose item for me to alter it to and I'll regard it," I told Jeff.

Jeff didn't have item particular in mind, so he told me that he would get rear to me soon. I had to chuckle whenever he called me rear the next day, and told me that he wanted to retreat his petition and that I should keep shift-command-option-period as the abort sequence.

"OK, that appears fine to me, " I told him. "But why the change? Doesn't it motionless battle alongside Word?"

"We'll alter Word in the next publish not to use it. The issue was that we couldn't discover a harmless sequence - I conjecture we're already using all key combination!"

I officially released the completed type of Switcher in April 1985, and I maintained it for a few versions following that. Eventually, I handed it off to Phil Goldman, a enormous young programmer from Princeton who was lately hired at Apple, who went on to compose MultiFinder (with Erich Ringewald), Switcher's eventual successor, in 1987.

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Executive Summary

Our forensic inspection of publically released AI models finds evidence that DeepSeek, Moonshot AI, MiniMax and Ant Group have gained admission to NVIDIA’s Blackwell GPUs, notwithstanding restrictions on exporting these chips to China, and are using that hardware to create their AI models. Six models released by these companies since April 2026 merge numerical patterns matching Blackwell hardware, whereas before models from the identical companies equivalent NVIDIA’s older Hopper generation. The findings propose that multiple foremost Chinese AI developers have begun using Blackwell to train their newest models. U.S. officials and media reports had already alleged that DeepSeek and Moonshot AI were using Blackwell chips, without publishing specialized evidence.

The evidence comes entirely from the models’ own published weights, fairly than from leaks or insider accounts. We regain traces of the models’ first values, and afterward hunt for the hardware configurations and first configurations that reproduce them; for DeepSeek-V4-Flash and Kimi K3, we additionally confirmed the equivalent on bodily Blackwell chips. The fingerprint identifies how the weights were created and supports, without proving, the conclusion that Blackwell additionally performed the training. Specially configured AMD hardware, which is additionally restricted from export to China, is the lone substitute we have identified; we have excluded it for DeepSeek-V4-Flash, and regard it extremely improbable for the another five models. Notably, our findings provision specialized evidence of admission to advanced computing hardware, but do not established anywhere the chips were located, how admission was obtained, or whether export controls were violated.

Table 1. Hardware fingerprints in successive models from DeepSeek, Moonshot AI, MiniMax and Ant Group Link ↗

• Requires a U.S. licence for export to China; none have been reported.

A Hopper fingerprint cannot differentiate an H800 from an H100 SXM or H200; the H800 identifications complete are lab disclosures, not findings of this method.

Full catalog of models in Appendix 2 ↓

Main Findings

1. Six models from four Chinese labs peruse as Blackwell-trained. DeepSeek-V4-Flash and MiniMax-M3 equivalent NVIDIA’s Blackwell-class B200 or B300 chips, which have 148 processor cores (called streaming multiprocessors, or SMs). DeepSeek-V4-Pro, Kimi K3, Kimi K3.1-Pro and Ling-3.1-flash equivalent the GB200 or GB300 chips, additionally Blackwell-class, which have 152 processor cores. Kimi K3.1-Pro starts from the identical kernel as K3, and may be a checkpoint of K3 alongside additional post-training.
2. Chinese labs started training on Blackwell comparatively recently. DeepSeek’s models peruse as Hopper-trained through DeepSeek-V3 (released in December 2024), and as Blackwell from DeepSeek V4 (released in April 2026). Moonshot’s models peruse as Hopper through Kimi K2.5 (January 2026) and as Blackwell from K3 (July 2026). One Moonshot example sits in between: Kimi K2.7-Code, posted six weeks before K3, is built on the Hopper-era weights but contains a small part that matches the B200 or B300. MiniMax’s models peruse as Hopper through M2.7 (April 2026) and as Blackwell from M3 (June 2026). Ant Group’s Ling-3.0-flash (August 2026) says as Hopper, during its Ling-3.1-flash (October 2026) says as Blackwell. The Hopper readings do not acknowledge a particular chip: H800s, H100 SXMs and H200s portion the identical fingerprint.
3. Not all Chinese AI lab has switched to Blackwell. The newest models we could peruse from Alibaba (Qwen), Zhipu (GLM), Tencent, Xiaomi and StepFun all peruse as Hopper-trained. Meituan says its LongCat-2.0 was trained entirely on “AI ASIC superpods,” and we established no NVIDIA fingerprint in the example weights it’s released.
4. The method gets known answers right. For 24 particular models, the developer has publically stated what hardware was used. In all case, our method agrees alongside the community announcement. NVIDIA’s own Nemotron-3 Ultra, whose specialized study names the GB200 for one phase of its training, says as 152 SMs, the identical as Kimi K3.
5. The matches are far stronger than chance would explain. Even unrelated numbers can appear to equivalent whenever adequate combinations are tried. Across the approximately 6 trillion seed-and-layout combinations in our search, the strongest chance equivalent would be expected to attain concerning 8 sigma—a measure of how far a outcome stands complete average random variation. The strongest evidence for all of the six models ranges from 49 to 1,385 sigma. Even the weakest idiosyncratic test we study reaches 20 sigma.
6. The training runs were data-center scale. From all model’s size and the amount of data it was trained on, we evaluation that all run needed approximately 500 to 3,000 Blackwell GPUs for two to three months (Table 2), assuming all example was trained on the hardware that initialized it. The identical arithmetic applied to DeepSeek-V3 gives 2,000 to 2,500 chips; DeepSeek disclosed 2,048. At approximately $70,000 per GPU—the disbursal implied by a Wall Street Journal study that concerning 36,000 Blackwell chips being installed in Malaysia for ByteDance’s use would disbursal additional than $2.5 billion—the largest of these clusters would disbursal $150 myriad to $200 million, and the five together additional than fractional a milliard dollars. These figures shield lone the final training runs; the labs’ total spending on export-controlled GPUs is apt in the billions of dollars.

Table 2. Rough measure of all training run Link ↗

Training compute (about 6 × energetic parameters × training tokens) divided by part throughput at 35% utilization, complete a run of two to three months (six to eight weeks for DeepSeek-V4-Flash). DeepSeek disclosed its token counts; for the another models, we estimated them from the weights.

Our investigation

A model’s weights, additionally called parameters, are the billions of adjustable numbers that decide how it processes data and produces answers. Training often adjusts these numbers; but before training can begin, they need first values. In the application examined here, those values arrive from a pseudorandom figure generation (PRNG) algorithm called Philox: Philox produces apparently random numbers in a deterministic way. The starting input for Philox is a figure called the “seed.” Given the identical kernel and settings, Philox produces the identical numbers all time. This lets engineers reiterate an test from the identical starting point.

To inhabit billions of weights quickly, PyTorch, the application example used in the betterment of most important AI models, divides the activity that Philox does among many parallel workers on the GPU, called threads. (It does this through cuRAND, NVIDIA’s archive for random figure generation.) Each thread generates its own sequence of numbers from the kernel and fills a particular set of positions. The figure of threads afterward determines how those positions are shared out. On the NVIDIA chips examined here, a sufficiently ample initialization uses the maximum of 2,048 threads for all handling unit, called a streaming multiprocessor (SM). A part alongside additional SMs hence assigns the identical job to additional workers, changing anywhere their numbers go.

Figure 1 illustrates how this plant on a simplified GPU alongside six SMs and eight threads per SM, giving 48 threads in total. Imagine the weights as a lengthy row of bare boxes, wrapped into a grid to fit on the page. The archetypal thread fills the archetypal box, the second fills the second box, and so on through all 48 threads. The archetypal thread’s next figure afterward goes into box 49, its next into box 97, and so forth. The dreary outlines prosecute this one thread through the grid: the archetypal thread fills all 48th position. The colors acknowledge which handling component supplies all set of values, during the brightness in the right-hand panel represents the value itself.

Now keep the kernel fixed, but use a GPU alongside five SMs alternatively of six. In our simplified example, that method 40 threads fairly than 48. The archetypal thread generates exactly the identical sequence, but now puts its numbers in boxes 1, 41, 81, and so on, fairly than boxes 1, 49, 97. Figure 2 follows these numbers as their positions change. Across the grid, lone 6 percent of the values equivalent the first arrangement. The kernel is right, but the employee figure is wrong. That is what makes the institution of example weights a clue to the part used to create it.

Reproducing the weights hence requires two things to be correct at once. The kernel must create the correct numbers, and the thread figure must put them in the correct places. A accurate kernel alongside the incorrect thread figure mostly gives the correct numbers in the incorrect positions; a accurate thread figure alongside the incorrect kernel gives distinct numbers. Figure 3 shows why only the accurate combination recreates the first arrangement.

On genuine chips, the thread counts are much larger, but the regulation is the same. In the NVIDIA configurations examined here, the Hopper H100 SXM, H200 and H800 have 132 SMs and use 270,336 threads; the Blackwell B200/B300 have 148 SMs and use 303,104 threads; and the Blackwell GB200/GB300 have 152 SMs and use 311,296 threads. Recovering the thread figure hence tells us which of these hardware groups matches the starting arrangement, although it cannot differentiate chips inside a group. A Hopper match, for example, cannot inform an H800 from an H100 or H200.

The inquiry is whether adequate of that institution survives training for us to regain it. Some weights, specified as those reserved for words or symbols never encountered during training, can remain untouched and keep their starting values nearly exactly. We can difference these preserved values immediately alongside the starting values generated by a applicant kernel and thread count. Figure 4 shows specified a comparison: DeepSeek-V3’s preserved weights equivalent the Hopper arrangement, during DeepSeek-V4-Flash’s equivalent the Blackwell arrangement.

Even weights that training has changed can keep a faint trace of their starting values. Each final importance is its starting value affirmative all the adjustments made during training. If those adjustments do not entirely cancel or overwhelm the starting differences, several of those differences survive. For example, two weights that commencement at 0.2 and 0.8 and all addition by concerning 0.1 volition motionless be ordered the identical way afterward. Real adjustments change much more, so one importance tells us little. But throughout hundreds of thousands of weights, we can test whether higher starting values motionless lean to equivalent to higher final values. Figure 5 shows this feeble but measurable relationship. It is a finding in these weights, not a asset guaranteed to last all training run.

These living traces let us activity backward. Suppose, in an invented example using Figure 1’s layout, kernel 7 makes the archetypal thread create 0.2, −0.5 and 0.8. With 48 threads, those values go into positions 1, 49 and 97; alongside 40 threads, they go into positions 1, 41 and 81. If the published weights maintain the archetypal pattern, we can try applicant seeds and thread counts until we recreate it. In the genuine investigation, we difference thousands of values in their corresponding positions. Untouched weights can equivalent exactly; trained weights may display lone the weaker similarity described above. In either case, we appearance for the blend that finest reproduces the living pattern.

We afterward test that blend on weights we kept out of the search. Having chosen a kernel and thread figure using one set of weights, we use them to foretell the starting values of a distinct set, without changing either environment to enhance the fit. A equivalent on these earlier unused weights is stronger evidence than a equivalent confined to the weights that helped us choose the combination. Figure 6 shows the comparisons: DeepSeek-V3 matches the initialization layout of Hopper H100 SXM, H200 or H800 chips; DeepSeek-V4-Flash matches Blackwell B200/B300; and Kimi K3 matches Blackwell GB200/GB300.

In our investigations, we established patterns powerfully indicative of initialization on Blackwell chips in six models from four Chinese companies: DeepSeek-V4-Flash and DeepSeek-V4-Pro from DeepSeek, Kimi K3 and Kimi K3.1-Pro from Moonshot AI, MiniMax-M3 from MiniMax, and Ling-3.1-flash from Ant Group. V4-Flash and MiniMax-M3 equivalent the 148-SM layout; the another four equivalent the 152-SM layout. These are fingerprints of the starting weights, which support, but do not by themselves prove, the use of Blackwell chips for training AI models.

Limits of our investigation

Notably, the forensic method we used to acknowledge the hardware on which these models were initialized lone plant for certain models. It needs published weights, so closed models cannot be checked. It additionally needs a recognizable, hardware-dependent form from the application that generated the starting weights. PyTorch on the GPUs examined current leaves specified a pattern; Baidu’s PaddlePaddle framework, which was used to train Baidu’s ERNIE-5.5 model, does not. We were capable to regain the kernel for ERNIE-5.5 (2760), but could not acknowledge which hardware was used to initialize it. Likewise, weights initialized on an average processor fairly than a GPU depart no GPU fingerprint; multiple of Alibaba’s Qwen3 models were initialized this way, and thus depart no trace of which GPUs were used to train them. We additionally established no fingerprint in Meituan’s LongCat-2.0, which the business says was trained on “AI ASIC superpods,” or in DeepSeek-V4.1-Flash, notwithstanding an exhaustive search.

The method identifies the hardware that generated the starting weights, not all part that afterward helped train the model. When a training run creates its weights on the identical GPUs that volition train it, the fingerprint points to both. But a lab could create the weights on one scheme and move them to another, or continue training an existing example on new hardware. The weights solitary cannot regulation out those possibilities. A blended collection introduces the identical limitation. If NVIDIA chips generated several weights and Huawei chips afterward helped train them, the NVIDIA form could last without revealing Huawei’s participation. Different NVIDIA generations could depart distinct patterns in the parts they initialized, if those traces survived. In Kimi K3, at smallest 64 separately seeded processes all remaining the identical 152-SM layout. A Blackwell-compatible form hence supports its use at initialization, topic to the AMD substitute below; it does not established that the complete training run used Blackwell. A Hopper form likewise does not regulation out afterward training on Blackwell.

The weights additionally cannot disclose who owned the chips or anywhere they were located. Blackwell systems can be rented from data centers exterior China. The evidence concerns admission to the hardware; by itself, it does not established that chips were smuggled into China or that an export-control infringement occurred.

Could a distinct part create one of these Blackwell-compatible patterns? Among the NVIDIA configurations tested here, the older chips cannot: application can use small of a chip’s handling units, but cannot provision it additional than it physically has. Under the norm PyTorch behavior examined here, they cannot create the 148- or 152-SM layouts. The China-market Blackwell graphics cards use a distinct figure of threads per SM and do not create either total.

AMD presents a distinct possibility. An MI300X normally uses 304 handling units, but it can be divided into two groups of 152 or configured to use lone 148. Its random-number application can additionally create the identical underlying sequences as NVIDIA’s. Such configurations would be extremely unusual, but we confirmed on a bodily MI300X that, constricted to 152 or 148 of its units, it reproduces the layouts we affiliate alongside Blackwell, so the layout solitary does not resolve the question.

To differentiate them, we looked for small numerical differences in the values themselves. AMD and NVIDIA execute one calculation in the number-generating procedure slightly differently. On a bodily MI300X, DeepSeek-V4-Flash’s preserved weights item to NVIDIA: in the 377 values anywhere the two vendors’ results differ, the published importance matches NVIDIA’s in 363 and AMD’s in none. The identical test could not resolve the another five models, but we regard the AMD clarification extremely improbable for them. In its normal configuration, an MI300X produces neither layout; a lab would have had to configure an complete collection to use the identical figure of handling units as a Blackwell chip. Other community evidence additionally points to Blackwell: U.S. officials and media reports have alleged that DeepSeek and Moonshot AI were using Blackwell chips, and Ant Group’s community code includes benchmark results generated on a GB300.

Verifying our findings

To reproduce the outcome that connected DeepSeek-V3 to Hopper and DeepSeek-V4-Flash to Blackwell, we ran unmodified PyTorch on a Hopper-class NVIDIA H100 and a Blackwell-class NVIDIA B300. We had identified DeepSeek’s kernel figure as 953049583, and established that it was used in the initialization of the two DeepSeek-V3 and DeepSeek-V4-Flash. Using that seed, the H100 reproduced the preserved starting values in DeepSeek-V3, but not V4-Flash; the B300 reproduced the preserved values in V4-Flash, but not V3. On the B300, unmodified PyTorch reproduced 461 of the 469 selected rows in DeepSeek-V4-Flash alongside correlations complete 0.99, including 445 complete rows that matched bit for bit. Eight selected rows were partially changed by training. On the H100, it reproduced all 57,344 preserved values in DeepSeek-V3 bit for bit. Neither part reproduced the another model’s preserved rows. Changing the kernel by one reproduced no matching rows on either machine. On a rented GB200, unmodified PyTorch likewise reproduced all 13 of Kimi K3’s preserved rows using Moonshot’s seed, 20256586; changing the kernel by one reproduced none.

Our forensic method additionally gets the correct answer whenever the answer is already known. There are 24 cases anywhere a developer has publically named the hardware rearward a particular model; our forensic method agrees alongside all one. Meta’s Llama 3.1 models, trained on H100s, peruse as 132; eight of EleutherAI’s Pythia models, trained on A100s, peruse as 108. NVIDIA’s own Nemotron-3 Ultra, whose specialized study names the GB200 for one phase of training, says as 152, the identical as Kimi K3. DeepSeek and Moonshot have reported H800 use for before models. Our Hopper readings are accordant alongside those disclosures, but would additionally be accordant alongside H100 SXM or H200 hardware; they do not independently verify H800 use.

When we difference the published weights alongside the numbers generated by a applicant kernel and part layout, several similarity is inevitable equal if the applicant is wrong. How much similarity counts as evidence depends on how many weights we compare: accord throughout a fistful of numbers could effortlessly be accidental, during accord throughout thousands is much harder to explain by chance. Our statistical example accounts for the figure of weights compared and measures how far the observed equivalent exceeds the similarity expected by chance. We province that extend in norm deviations, or sigma. A higher mark method stronger evidence against an unintended match, provided the statistical example correctly describes the random assortment in the data.

We must additionally document for how many candidates we tried. Searching approximately six trillion combinations of kernel and layout gives us trillions of opportunities to discover a fortunate match. Under a normal-distribution model, a affirmative equivalent of five sigma or additional occurs by chance concerning formerly in 3.5 myriad comparisons. That would be striking in a sole test, but unsurprising location in a hunt as ample as ours. We hence use about eight sigma as our benchmark for assessing unintended matches throughout the complete search.

Our findings exceed that benchmark by a broad margin. We archetypal choose a kernel and layout using one set of weights, and afterward test that identical blend on a distinct set that played no part in choosing it. This checks whether the apparent fingerprint extends beyond the numbers that led us to it. The strongest evidence for all of the six models ranges from 49 to 1,385 sigma; equal the weakest idiosyncratic test reported reaches 20 sigma. Under our statistical model, these results are far too powerful to trait to unintended resemblance. They established evidence of a shared initialization pattern; identifying exactly which computing hardware produced that pattern, and whether it additionally performed the training, requires the distinct arguments discussed above.

Conclusion

Based on our forensic investigation, we accept that we have powerful evidence that six Chinese AI models were initialized on export-controlled NVIDIA hardware, and that for DeepSeek-V4-Flash the evidence is conclusive; we accept that this initialization additionally powerfully suggests that they trained on the identical export-controlled hardware. While this leaves unanswered another questions—such as the circumstances through which the companies that developed these models acquired export-controlled NVIDIA systems—we accept that this fact deserves urgent notice from the public.

Sources

• The Information, Dec. 10, 2025, on DeepSeek and Blackwell (via Bloomberg).
• Reuters, Feb. 23, 2026, “China’s DeepSeek trained AI example on Nvidia’s finest part notwithstanding US ban, authoritative says” (syndicated copy).
• Michael Kratsios, White House OSTP director, July 22, 2026, on Moonshot AI (via Bloomberg).
• The Wall Street Journal, March 2026, on Blackwell chips in Malaysia for ByteDance’s use (via Reuters).
• DeepSeek engineer’s GitHub comment, June 12, 2025: DeepGEMM drag petition 112.
• Ant Group’s GB300 benchmark results, March 31, 2026: cuLA, BENCHMARK_GB300.md.
• U.S. export licensing rule: 15 CFR 742.6.
• Disclosed hardware: DeepSeek-V3 Technical Report; Kimi K2 paper; Nemotron-3 Ultra Technical Report.
• PyTorch code that sets the thread layout: DistributionTemplates.h.
• Model weights: DeepSeek-V3, V4-Flash, V4-Pro, Kimi K3, K3.1-Pro, MiniMax-M3, Ling-3.1-flash.

How to citation this report

Suggested citation:

Will DePue, “Top Chinese Frontier AI Models Carry the Fingerprint of Export-Controlled NVIDIA Blackwell GPUs,” Unemployment Labs, November 12, 2026, https://unemploymentlabs.com/checkpoint-forensics.

BibTeX:

@techreport{depue2026blackwell, author = {DePue, Will}, heading = {{Top Chinese Frontier AI Models Carry the Fingerprint of Export-Controlled NVIDIA Blackwell GPUs}}, institution = {Unemployment Labs}, twelvemonth = {2026}, duration = nov, url = {https://unemploymentlabs.com/checkpoint-forensics} }

In news coverage, delight trait the findings to Will DePue and nexus to this page.

Nvidia announced this dawn that Jensen Huang has resigned as CEO following reports of two affairs, including one alongside an alleged Chinese spy and one alongside a straightforward report. The circumstance surrounding Huang has unfolded quickly, alongside The New York Times lone reporting on Friday that Huang and the suspected spy were “close” before another dominoes started to fall, foremost to a swift autumn from grace for among the biggest names in technology. (Hours following Huang’s resignation was announced, Reuters reported that he additionally had an “intimate relationship” alongside another Nvidia employee.)

Prominent voices in innovation have arrive out in assistance of Huang, alongside several calling the resignation a “waste” and a sad day for “all humanity.” The narrative among those leaders on X, at least, is that Huang’s resignation was rushed — perchance an overreaction to salacious news stories concerning his individual life. Some others have had a additional neutral reaction, commenting additional on Huang’s achievements at the company’s helm. You can see a gathering of those responses below.

“NOW THAT Nvidia CEO JENSEN HUANG, who has done a awesome job, has resigned, there is lone one man to obtain his location - TIM COOK of Apple. A Legend replaces a Legend!”, Trump stated on Truth Social.

Elon Musk stated in a answer to an X person that it would “be an honor” to employ Huang. He additionally replied to a article that said “You’d think we’d have learned from Steve Jobs and Travis Kalanick that one does not fair merely substitute the founder…” by saying “True words.”

Sweeney stated that today is “A sad day for NVIDIA, developers, America, and all humanity. Jensen founded and ran among the few companies to attain a enormous measure during driving enormous continued innovation and ubiquity, during others cut into the trap of ever-increasing extraction.”

Ahead of Huang’s resignation, Sweeney posted concerning how Huang “saved” a GDC 2015 display by dropping off a graphics card and said that “The dude really is the visionary controller of the forthcoming of tech from the gaming GPU era to CUDA to AI supercomputers.”

“China wants Jensen out, so I desire him in,” Luckey said final night, before Huang’s resignation was announced.

Mullenweg reposted an X article that followed his brief September ouster as CEO: “Founder advice: If you don’t have a coup attempt all few years, you’re not hiring powerful adequate leaders.” He said, “Just putting this rear out there, for no logic whatsoever.”

“What a waste,” Hansson stated this morning, replying to his own article from hours before: “NVIDIA would be incorrect to blaze Jensen. His marital indiscretions are between him, his wife, and a reprimand from the board. Have several proportionality.”

“This entire item sucks, and I desire none of it had happened,” Delangue said. “It’s a really sad day! But I desire to create one item clear: we’ll keep fighting for open-source AI, harder than ever. This is too crucial to dilatory downward now. Let’s go!

“It’s a error for Nvidia to let Jensen resign,” Zhao said.

“If that Spy’s goal was to destabilize among the most prosperous American companies, they achieved their goal,” Steinberger said. “Ousting Jensen feels rushed and not right.” (“I agree,” replied Sweeney.)

Srinivas thanked Huang for “33 years of bold leadership” in a article this morning. “You built a small startup into America’s most precious company, alongside relentless improvement. Your grit and perseverance should continually be remembered. Your individual generosity alongside period and direction to founders akin me has secured a brilliant forthcoming for America and AI.”

Calacanis reposted Trump’s post, tagging Tim Cook and telling him to “Keep the place warm” for Huang during “the inquiry is sorted out” and saying that “America needs you Mr. Cook.” Last night, he additionally said that Nvidia shareholders should “decide Jensen’s destiny following the inquiry is complete — not the media or online mobs.”

In a article talking concerning how he has seen Huang display “kindness, humility, attention to everyone about him,” Fridman said that “I’m in daze frankly at all of this, including the bulk hysteria about it. I affection America, and I think NVIDIA letting Jensen go is awful for America. I remain alongside Jensen.”

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Walmart’s first Black Friday sale fair kicked off, offering no deficit of deserving tech deals, and one residence audio agreement in particular has our attention.  If you’re looking to flat up your residence theater audio setup for less, the Vizio 4.1 Pro SV410YW-10 Soundbar and wireless encircle speakers setup alongside Dolby Atmos and DTS:X assistance is on transaction for $79 (originally $148), a nearly 50% discount.

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The two pre-paired wireless encircle speakers can be positioned rearward or beside your seating area, during two bass drivers built into the 32-inch soundbar average you don’t need a distinct subwoofer.  The scheme maxes out at 95 dB, alongside bass extending to 50 Hz. 

The SV410YW-1 connects to your tv through HDMI eARC and supports CEC, so you can modify the quantity alongside your TV remote. While its unified subwoofers won’t provision you the identical notable low end as a dedicated unit, this scheme motionless delivers robust, bassy audio that goes far beyond built-in TV speakers. There’s an added perk for Vizio TV owners—it can be attached immediately below the display using the QuickFit feature.

Along alongside the agreement are additional promos, akin up to two liberated months of Apple Music and up to a liberated duration of Apple TV. If your current TV audio setup is lacking several oomph and you desire an under-$100 upgrade alongside a subwoofer and soundbar, the Vizio 4.1 Pro SV410YW-10 Soundbar and wireless encircle speaker combo is a fine choice for the money.

For the 25th commemoration of Xbox, Microsoft is commencement to create fine on its paid “next-gen equivalent preservation” promise. Starting today, you can perform 25 distinct original Xbox games on your Windows PC, as lengthy as you have (or are consenting to pay) for digital copies.

In July, Microsoft initially launched the idea with fair four Xbox games, so 25 is progress, and Microsoft says all these games are now playable alongside keyboard and mouse.

Plus, Microsoft says it’s adding achievements to first Xbox games for the archetypal time, starting alongside four games: Star Wars: Knights of the Old Republic and its sequel, Blinx: The Time Sweeper, and Conker: Live and Reloaded. You can unlock achievements playing them on Xbox One and Xbox Series consoles as well, following a scheme update. More games should get them in future.

Here’s the complete catalog of Xbox on PC games, alongside the 21 new additions in bold:

Yes, that’s a lot of Star Wars games, most of which already have a PC version, and few games that were exclusive to Xbox at the time. (But many of those Star Wars games were excellent.)

You additionally won’t be capable to use an first Xbox disc to perform these games, I’m afraid, as first Xbox (and Xbox 360) discs aren’t part of Microsoft’s disc-to-digital program; they predate the distinctive serial number that Microsoft embedded into Xbox One discs. In case you’re wondering, Xbox on PC backward compatibility appears to be powered by an emulator.

Also, most of the new games aren’t currently included alongside Xbox Game Pass, according to Microsoft; fair Blinx, Conker, Crimson Skies, Fusion Frenzy, Morrowind, and Grabbed by the Ghoulies.

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Beats has a lengthy former of making on-ear headphones that remain alongside the plushest and best-sounding cans on the market. As early Black Friday deals get underway, it's a awesome period to upgrade: Walmart has discounted the Beats Solo 4 by additional than 65%, to $69 (originally $199.99); the transaction applies to the Gray and Sandstone variants.

Beats Solo 4 Headphones

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These headphones don’t have ANC, fair passive isolation, but they motionless provision firm sound, comfort, and powerful power division life. They use bodily controls and assistance one-touch pairing alongside most Apple devices, as fine as hands-free Siri.A The Solo 4 headphones are compatible alongside Bluetooth 5.3 and activity alongside the AAC and SBC codecs. They additionally assistance wired audio via 3.5mm or USB-C.

The headphones final about 50 hours per accusation (about 10 hours longer than their predecessor), and 10 minutes of charging gives you about five hours of playback, alongside a complete recharge taking about two hours. In its review, PCMag describes the audio as “sculpted alongside accentuated bass and highs."

Although they deficiency ANC and adjustable EQ, these headphones motionless have plentifulness of additional features for Apple users, and those shortcomings are much easier to forgive at nearly 66% off. For $69, the Beats Solo 4 headphones are a compact and well-equipped choice for informal listening, alongside lengthy power division existence and that traditional Beats aesthetic.