DeepSeek V4 Preview launch: 1M-context MoE on Huawei Ascend

Strip away the benchmark numbers and what V4 actually proves is structural: for the first time, a frontier-class open-weight model launched with same-day inference support on Huawei Ascend NPUs and Cambricon accelerators, with the integration code open-sourced to GitHub. That is a different category of event than 'another Chinese model release'. R1 in January 2025 made Silicon Valley nervous about Chinese training efficiency; V4 makes Silicon Valley nervous about something harder to fix — a Chinese end-to-end stack from chip to compiler to model that ships on the same calendar day as the model itself.

This is the scenario Jensen Huang named out loud: 'the day that DeepSeek comes out on Huawei first, that is a horrible outcome' for the United States. April 24 was not literally that day — Liu Zhiyuan of Tsinghua notes V4's main pre-training was likely still done on NVIDIA hardware, and only parts of the training were adapted to Chinese chips. But the inference path, where the dollars actually live for a deployed model, is now demonstrably non-NVIDIA-feasible at flagship quality. Huatai Securities' brokerage desk read it as a direct catalyst for domestic-chip adoption in 2026; the SMIC stock jump of roughly 10% the same day suggests the equity market agrees. The geopolitical signal is the lead, not the lagging indicator.

The pricing — V4-Pro at $1.74 / $3.48 per million input/output tokens, V4-Flash at $0.14 / $0.28, versus GPT-5.5 at roughly $5 / $30 — looks like a marketing decision but is actually an architectural one. The new Hybrid Attention combines Compressed Sparse Attention (CSA), which lets the model only attend to the small fraction of past tokens that matter for the current step, with Heavily Compressed Attention (HCA), which stores those tokens in a much smaller per-token memory footprint. The result, at a 1M-token context, is roughly 27% of V3.2's inference FLOPs and just 10% of its KV cache (the per-token memory the GPU has to hold while generating). V4-Flash is even more aggressive: 10% of FLOPs and 7% of KV cache. The Register's measurement frames this as a 9.5x to 13.7x reduction in memory needed to serve million-token contexts.

In practical terms, that is what makes the cents-per-million numbers physically possible. A 1M-token agent run — paste an entire codebase, ask the model to find a bug, watch it reason across the whole repo — was uneconomic at V3.2 efficiency on any provider's hardware. It is now roughly an order of magnitude cheaper to serve, which is why the Mixture-of-Experts choice (only 49B of 1.6T parameters fire on any given token for V4-Pro, only 13B of 284B for V4-Flash) compounds with the attention redesign rather than competing with it. The 73% headline 'cost reduction vs. prior generation' is the accounting consequence of these two design choices stacked. For closed-source incumbents, the uncomfortable read is that DeepSeek isn't winning on subsidy — it's winning on the inference math.

Buried in §5.4.4 of the V4 technical report — and the line the r/LocalLLaMA launch thread fixated on — is a quietly extraordinary internal-adoption claim: 52% of DeepSeek's own engineers say V4-Pro is now their primary coding model, with another 39% leaning that way. That is the kind of dogfooding receipt Anthropic and OpenAI tend not to publish, and it does more credibility work than any benchmark line on MMLU-Pro or LiveCodeBench. Independently, Artificial Analysis ranked V4-Pro #1 among open-weight models on its GDPval-AA agentic real-world evaluation and #2 overall on its Intelligence Index behind only Kimi K2.6, and SGLang shipped Day-0 inference optimization. None of that lives in the same epistemic register as a self-reported MMLU score.

The community reaction split immediately along this fault line. The viral LocalLLaMA 'Deepseek v4 people' thread spent its top comments dismissing V4's correct answer to a classic 'winter tires' reasoning gotcha as memorization rather than reasoning — 'It's in the data at this point' — a healthy reminder that contamination skepticism scales with a model's perceived strength. On r/singularity the dominant frame was simpler: DeepSeek is 'smelling blood in the water' the same week OpenAI doubled GPT-5.5 prices. AnythingLLM's Tim Carambat, in YouTube commentary, argued the CSA/HCA attention design is the more important story than any of the benchmark scoreboards — which, given the price math above, is the read that ages best.

The most interesting tell is what didn't happen. In January 2025, R1 and V3 took roughly $590B off NVIDIA's market cap in a single news cycle. V4 — a more capable model, on Chinese silicon, at lower prices — produced a far more contained reaction concentrated in Chinese names: SMIC up around 10% in Hong Kong, MiniMax and Knowledge Atlas down more than 9% as investors rotated out of competing Chinese labs presumed to be on the wrong side of the price collapse. NVIDIA did not crater this time.

Two readings. The bearish one for NVIDIA: the market has already priced in a world where Chinese AI runs on Chinese chips at the margin, so the V4 confirmation is news but not surprise. The bullish one for DeepSeek: the launch is being absorbed as structural rather than episodic — not a one-off shock but the next datapoint in a now-established trend line. White House Science & Technology Advisor Michael Kratsios responded with the standard framing that 'there is nothing innovative about systematically extracting and copying the innovations of American industry,' which is the kind of statement governments make when capital markets have already moved on. Either reading is harder for closed-source flagship API providers than for chipmakers, which is also where the immediate competitive pressure now sits — on token pricing, not on training compute.

Three things worth holding in tension with the headline. First, Tsinghua's Liu Zhiyuan's caveat that V4's main pre-training was likely still done on NVIDIA hardware: the Chinese-stack story is real for inference and partial for training, and the gap matters because training is where the export-control bite is sharpest. 'Day-0 Huawei support' is a deployment claim, not a training-independence claim, and the two will keep getting conflated in coverage.

Second, on local hosting: the LocalLLaMA thread reality-checked the implied accessibility. V4-Flash needs 256GB+ of RAM despite only 13B active parameters, because Mixture-of-Experts models still have to load all expert weights into memory even when most don't fire per token. 'Open weights' is not the same as 'runs on your gaming rig.' Third, the contamination skepticism on Reddit isn't paranoid — when a model nails every classic reasoning gotcha, the prior on memorization rises mechanically, and the more interesting public evals (Artificial Analysis GDPval-AA, where V4-Pro ranked first among open weights for agentic real-world tasks) are doing more truth-telling work than any single MMLU-Pro number. The launch is genuinely consequential; it is also the moment when the next twelve months of independent replication and adversarial probing actually start.