DeepSeek Commits New Funding to Nvidia and Huawei Chips, Caps 2026 Compute Spend at 20 Billion Yuan
DeepSeek will convert nearly all of its latest funding into graphics processing units from Nvidia and Huawei within six months, stating it is willing to pay a premium to secure hardware for training models operating on 800B activated parameters. The company already holds 20,000 H100-equivalent units and expects total compute spending for 2026 to cap at 20 billion yuan.
The hardware expansion underpins a strategy centered on open-source model development. While current infrastructure relies on Nvidia hardware, the company will migrate training workloads entirely to Huawei chips by rewriting Nvidia's software environment. Huawei can currently provide capacity for roughly 16,000 GPUs to the company, with management predicting that broader domestic chip capacity and ecosystem parity will arrive within a year.
From the sources (12 posts)
@poezhao0605DeepSeek closed a round of more than $7bn. Barely a month later, it began talks for a new round at a $71bn pre-money valuation. Its management has signaled that frontier research takes precedence over near-term revenue. My latest ⬇️
@teortaxestexFull transcript of Wenfeng's investor conference call. He's a lot more forceful here. He is insistent that open sourcing is *the* agenda. If you're not on board, go buy more Zhipu. And crucially, he says restraint is *the only way to surviv
@teortaxestexDeepSeek 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 willi
@teortaxestexLOTS OF ALPHA FROM WENFENG HERE He expects he won't be able to spend >20B RMB on compute in 2026 «The smartest people—maybe less than 50% stay in China» «The largest current model activates ≈800B parameters» «I'd need about 50K GB300s, o
@teortaxestexOn 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 mo
@teortaxestexOn 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 b
@teortaxestex950s 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 su
@teortaxestexLiang Wenfeng believes that the comprehensive gap in AI between China and the US is 12-18 months, just like Kai-Fu Lee says and Dario hopes; and can be shrunk to 3, with surpassing in some few key areas. @scaling01 @zephyr_z9
@teortaxestexThis is extraordinary. This is what I expected of China that gets serious about compute independence. A 14nm chip that has Hopper+ level utility. Yes it runs HOT, but it gets the job done. 6.4TB/s now. 20TB/s – Rubin level – in 2027. Electr
@teortaxestexRT @teortaxesTex: Liang Wenfeng believes that the comprehensive gap in AI between China and the US is 12-18 months, just like Kai-Fu Lee sa…
@poezhao0605The detail worth sitting with: China's flagship domestic GPU company gets its best inference numbers by pooling its own chips with foreign ones. Import substitution, built on top of the imports.
@teortaxestex«So we simply won't consider competing with the US at that scale now… when we have more resources later, we'll push to 150B, 156B, or 250B activation scale.» «You could force-train a model that big, but you couldn't do sufficient research»