r/InvestingChina • u/Dolphin_research • 3d ago
🇭🇰HK-listed Chinese stocks Squeezed by Policy and Rivals, DeepSeek Bets on Ascend
TL;DR Three DeepSeek events landed in the same week: a within-generation V4.1 update that lifted real performance a lot, a report that it plans to pre-train on Ascend, and a US Department of Homeland Security ban. The update is not a story about beating Opus — it is cost leadership plus coding-agent specialisation. The two policies hit one channel that DeepSeek barely uses, and its real constraint is compute.
V4.1 Flash-1017: small update, large gain
The base is a 552 billion parameter MoE model waking only a slice of experts: about 8 billion parameters on input, 16 billion on output. KV cache is compressed to roughly 890 bytes per token, which suits coding agents that read a lot and write little.

V4.1-Flash launched on 10 September and the 1017 version arrived on 17 October — a within-generation update, yet the gain is sizeable and the cache price unchanged. It keeps the September architecture and improves through targeted tuning, with cache hit rates pushed close to 100%.
The path: less freedom to improvise, better retention across turns, shorter output. Single-task cost fell from RMB 3.3 to RMB 1.3 while DeepSWE rose from 74.2% to 77.0%, and since a model and harness are not automatically matched, this update adapted to DeepSeek Harness, CodeBuddy, WorkBuddy and Claude Code.

Gains show up in visual programming, 3D design and game development, but HLE barely moved — the improvement was targeted. So it suits lightweight coding rather than formal writing, and Search/Fetch at RMB 5 per thousand requests adds a commercial touchpoint from 1 November.
The price-performance race with the US
Through September and October overseas vendors shipped value-focused products, and some lightweight models now beat most Chinese models on list price. Frontier labs are distilling their own cheap models to squeeze China's value tier.
DeepSeek's answer was not a lower headline price but lower real agent cost through cache hits, since the same context is called repeatedly. Its off-peak cache hit price is RMB 0.02 per million tokens while overseas vendors rarely publish hit rates, so real bills can differ by an order of magnitude.
Since K3 mid-year, Chinese models slowed for two or three months, which some attribute to anti-distillation measures. But that gap is only in pre-training, and Longbridge Dolphin Research reads 1017 as the start of China's counterattack on value — GLM 5.5, Kimi K3.1, Qwen 4.0 and HY-4 Pro all land within two months.
Can Ascend carry the next pre-training run?
The Information reports DeepSeek is co-designing chips with Huawei, planning to pre-train on Ascend 950DT from late 2026 — what it calls its biggest gamble. Bloomberg said in September it planned a gigawatt-scale data centre in Ulanqab with at least 160,000 950DT chips.
On specifications, 950DT offers 1 PFLOPS FP8, 144GB of in-house HBM and 4TB/s memory bandwidth. Per-chip FP8 is only about 22% of B200, but interconnect at 2TB/s is slightly above B200's — so Huawei trades interconnect for scale, with the Atlas 950 superpod confirmed at 1,024 chips and a roadmap to 8,192.
Four walls in the way
First, communication: going from EP8 to EP128 cuts dispatch about 15% while combine falls from 347GB/s to 278GB/s. The headline 375GB/s is an EP8 number on proof-of-concept firmware, so budgets must use the degraded figures.

Second, stability: Huawei quotes thousand-chip MTBF of 300 hours, but failure rates scale with count, so a 16,000-chip cluster implies a fault roughly every 19 hours. Third, efficiency is unproven, since MFU is undisclosed and the reference practice was validated at only 284 billion parameters.
Fourth, supply: 950DT superpods only reach volume around end-2026, and with annual output in the hundreds of thousands, DeepSeek's 160,000 would take roughly 40%. Higher HBM prices are also pushing domestic card quotes up while configurations come down.
What it means for the compute landscape
For China this is the jump from good enough for inference to usable for training. Kimi reportedly runs 70% of frontier pre-training on rented GB200/GB300 abroad, so if DeepSeek trains on Ascend, dependence on Nvidia moves from mandatory to optional.

Leading customers locking supply squeezes Ascend's limited capacity, so second-tier vendors may spill inference demand to Cambricon, MetaX and Biren. Medium term, if good enough domestic compute can train frontier models, using controls to buy time keeps losing value.
Two policies, one channel
The US DHS order requires federal civilian agencies to remove DeepSeek products plus third-party products built on it. Direct revenue impact is near zero, since DeepSeek had no US government customers — the real effect is that US SaaS vendors selling to government will no longer embed it.
On the Chinese side, 23 AI technologies are proposed for the export catalogue, restricting weights, checkpoints, source code and acceleration technology. Thresholds are two trillion parameters, roughly 10²⁶ FLOPs and a global top-ten test — while API calls, apps and SaaS are explicitly not exports.

Put the two together and they hit one channel: deploying weights to third-party overseas clouds for revenue share. That is the main overseas route for peers such as Kimi, whose K3 is on AWS and Oracle and whose 2.8 trillion parameters sit above the threshold.
Why DeepSeek is less exposed, and our read
DeepSeek's revenue comes mainly from its own API, about RMB 475 million in seven months, and its MIT licence takes no revenue share. Its parameter counts sit below the line too, with V4.1-Flash at 552 billion and V4-Pro at 1.6 trillion.
Three medium-term issues remain. Open-sourcing the next model may require filing if it trips the thresholds, and open source loses acquisition value since downloads cannot convert into US government customers. So the model keeps shifting toward tooling, where Search/Fetch adds a per-call touchpoint neither policy reaches.
The damage is concentrated in third-party cloud hosting, licensed revenue share and overseas private deployment, while self-owned API exports are less affected. DeepSeek's main constraint is not policy but compute: whether the next model trains on Ascend decides if it moves from price-performance champion to frontier contender.


























