some info from Wenfeng's investor conference call
On Huawei: «we participate deeply in Huawei's ecosystem» «Huawei gives us capacity for about 16,000 GPUs; internet giants might get over a 100K… but this may already be all the capacity Huawei has.» «So we can't count on training the next bigger model on Huawei, or training models with several hundred B activated parameters… But next year or the year after, there might be a chance.»
950s vs Nvidia: «when V3 trained, it still used NVIDIA GPUs, but no longer used NVIDIA's ecosystem… As long as I redo this whole process on Huawei GPUs, it's done. I think this might be a historic mission» «Huawei 950 supernode can fully substitute for NVIDIA's GB200 and GB300 in performance and price» «four Huawei GPUs equal one NVIDIA GPU, and it's two years behind… So our chip gap with the US, I believe, will no longer exist in ecosystems, but in chips it's four-fold plus two years.»
On domestic compute: bullish within a year. «Domestic AI chips have no problems in hardware or ecosystem—the only problem is insufficient production capacity» «Previously, domestic GPU adaptation had a problem called poor ecosystem… The moat of NVIDIA's CUDA is being rapidly dismantled, for probably three reasons. - with AI, building this ecosystem is much easier than before, because AI can write code. - second, some new technologies. For example, our company produced a technology called TileLang—a high-level language. Using this high-level language to write CUDA operators, you can quickly rewrite NVIDIA's entire ecosystem, and combined with AI, there seem to be no obstacles. - Another point: because CUDA—NVIDIA evolved from gaming GPUs, so in many places the gaming GPU design and settings carried through»
DeepSeek as of early June had 20K "H-equivalent" units (H100). Wenfeng intends to spend everything in 6 months, "basically all NVIDIA". «If we could convert all the money into GPUs, we'd convert every cent without hesitation—and we're willing to pay a certain premium for it»
Full thread here
On Huawei: «we participate deeply in Huawei's ecosystem» «Huawei gives us capacity for about 16,000 GPUs; internet giants might get over a 100K… but this may already be all the capacity Huawei has.» «So we can't count on training the next bigger model on Huawei, or training models with several hundred B activated parameters… But next year or the year after, there might be a chance.»
950s vs Nvidia: «when V3 trained, it still used NVIDIA GPUs, but no longer used NVIDIA's ecosystem… As long as I redo this whole process on Huawei GPUs, it's done. I think this might be a historic mission» «Huawei 950 supernode can fully substitute for NVIDIA's GB200 and GB300 in performance and price» «four Huawei GPUs equal one NVIDIA GPU, and it's two years behind… So our chip gap with the US, I believe, will no longer exist in ecosystems, but in chips it's four-fold plus two years.»
On domestic compute: bullish within a year. «Domestic AI chips have no problems in hardware or ecosystem—the only problem is insufficient production capacity» «Previously, domestic GPU adaptation had a problem called poor ecosystem… The moat of NVIDIA's CUDA is being rapidly dismantled, for probably three reasons. - with AI, building this ecosystem is much easier than before, because AI can write code. - second, some new technologies. For example, our company produced a technology called TileLang—a high-level language. Using this high-level language to write CUDA operators, you can quickly rewrite NVIDIA's entire ecosystem, and combined with AI, there seem to be no obstacles. - Another point: because CUDA—NVIDIA evolved from gaming GPUs, so in many places the gaming GPU design and settings carried through»
DeepSeek as of early June had 20K "H-equivalent" units (H100). Wenfeng intends to spend everything in 6 months, "basically all NVIDIA". «If we could convert all the money into GPUs, we'd convert every cent without hesitation—and we're willing to pay a certain premium for it»
Full thread here
