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AI chip domestic substitution process

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NVIDIA's China Share Crashes to 8% as Ascend Rises to 50%: The Dramatic Reshuffle of China's AI Compute Market in a September Report

· 5 min read
Industry Research Team

This article is based on market analysis published by Bloomberg Intelligence and Bernstein in September 2026 and public reporting; market share and revenue figures are analyst estimates, not official disclosures.

On September 28, two Wall Street reports simultaneously painted a dramatic picture: the balance of market share in China's AI compute market completed an almost total flip within 12 months.

1. The Share Flip: 40% to 8%, Ascend to 50%​

Bernstein's latest forecast:

  • NVIDIA's share of the China AI chip market will fall from about 40% to about 8% by the end of this year
  • Huawei Ascend rises to nearly 50%
  • The immediate trigger: China's September ban on new H20 orders from NVIDIA, which shut the last channel for NVIDIA's return to the Chinese market
  • ByteDance, Alibaba, and Tencent have all placed large Ascend orders

On the timeline, this was not a sudden shock but the cumulative result of three years of tightening export controls: NVIDIA's share of the China AI chip market once peaked at 95%; as high-end chips were cut off, Chinese hyperscale cloud providers pivoted wholesale to Huawei and domestic alternatives. Even when Washington allowed H200 sales to China in January 2026, the gears of that shift had already meshed — there was no going back.

2. Ascend 950PR: The "Chinese H200" in Analysts' Eyes​

What underpins this share data is product strength itself:

  • The Ascend 950PR entered mass production in March this year, with FP4 compute of up to 2 PFLOPS and 128GB of domestic HBM
  • Analysts rate it as roughly on par with the NVIDIA H200
  • Huawei's AI chip revenue is expected to reach $12 billion this year, up 60% from $7.5 billion in 2025

For the full specifications of the 950 series, see our earlier in-depth articles: Ascend 950PR/950DT Dual-Configuration Analysis and Ascend 960 Official Launch — the 960 was ready three quarters ahead of schedule, confirming a one-generation-per-year cadence.

3. The Model Side: Chinese Open Models Take the OpenRouter Token Pie​

While chip shares flipped, the model usage data is equally striking (OpenRouter platform statistics):

MetricData
DeepSeek token share (June)16.3%, surpassing Google, Anthropic, and OpenAI individually to rank first
Combined token share of Chinese open-weight models (May)About 61% (DeepSeek, Qwen, MiniMax, Tencent Hunyuan, etc.)
Weekly token consumption of Chinese modelsAbout 18 trillion, versus about 5.5 trillion for US models — a gap of more than 3x, with the overtake completed within a year
China-US frontier model performance gap (June)Narrowed to 6%, a historic low (9% in May)

Compute and models form a positive feedback loop: cards that cannot be bought force domestic compute to scale up; scaled-up domestic compute produces cheap tokens; and cheap tokens let Chinese open-source models swallow the bulk of global inference traffic.

4. But the Stock Market Isn't Buying It​

Bloomberg Intelligence pointed out a contradiction in the same period: the valuation discount of the China Tech 8 relative to the US Magnificent Seven has widened to more than 50%, the widest of the year, and BI believes only a "true AI breakthrough" can close it. Year-to-date stock performance: Alibaba about -7%, Tencent about -23%, while US AI infrastructure leaders broadly gained more than 15%.

There are two readings of this divergence: either the market is underestimating China's AI fundamentals, or earnings execution (Alibaba's EPS missed expectations by 17.3% last quarter, Baidu's by 36%) has dragged down the delivery of the AI narrative. For industry observers, the notable point is: the compute-side data (share, revenue, shipments) has moved ahead, while application-side monetization is still on the way.

5. Three Takeaways for Compute Buyers​

  1. For deployments in China, the Ascend ecosystem is already the default option. The cost of migrating from CUDA to CANN is one-time, while supply uncertainty is persistent; after the September ban, there is no compliant path to procuring new NVIDIA cards in the Chinese market.
  2. Domestic compute "matching the H200" is a watershed. The implicit assumption of domestic substitution used to be "discounted performance, discounted price"; when single-card performance catches up to the H200 generation, procurement decisions return to pure TCO and supply-security calculations — we recommend using the TCO Calculator to put electricity, depreciation, and utilization into the same table.
  3. The token cost curve determines the application landscape. Chinese models' 61% share of OpenRouter tokens shows that the chain of compute self-sufficiency leading to token price drops leading to application prosperity has completed its first leg; the next question is whether inference costs can keep falling.

Summary​

From 40% to 8% is not a slogan but a market reshuffle driven jointly by the ban, product strength, and the model ecosystem. For the Chinese market, the mass-production cadence of Ascend 950/960 and the maturity of the CANN ecosystem will determine whether this share can be held; for the global market, the combination of China's compute "internal circulation + open-source models going overseas" is rewriting the geographic distribution of inference traffic.

(Market share and revenue forecasts come from Bernstein / Bloomberg Intelligence analyst reports; actual figures are subject to each company's financial reports.)

Three Signals for the Domestic Compute Ecosystem in One Week: China Telecom Open-Sources Ascend-Trained Model, Inspur 128-Chip Inference Appliance, EVAS Raises RMB 2 Billion

· 5 min read
Industry Research Team

Beyond the official Ascend 960 unveiling and the Zhenwu V900 launch, late September also delivered three ecosystem signals that headline coverage easily drowned out, yet whose structural significance rivals flagship chips: a carrier open-sourcing a model trained on Ascend, an OEM delivering a hundred-chip-class domestic inference system, and a cloud-compute chip startup closing a new funding round. This article breaks down each one.


1. China Telecom Open-Sources Xing4.0: Carrier-Grade Endorsement for End-to-End Ascend Training​

On September 23 (as reported), China Telecom open-sourced Xing4.0-29B-A4B — an agentic mixture-of-experts (MoE) model that the company says was trained end-to-end on Huawei Ascend accelerators.

Why it matters:

  • Third-party proof that "it can train": until now, evidence that "Ascend can train large models" came mainly from Huawei itself (40+ natively trained models); a carrier running training with its own data, engineering teams, and clusters — and open-sourcing it — is the first scaled training case outside the Huawei ecosystem;
  • Agentic positioning: the 29B-A4B (29B total / 4B active) MoE form factor targets agent workloads directly — aligned with the open-source cadence of DeepSeek and Qwen, rather than a small experimental model;
  • Open-source spillover: publishing model weights plus the training recipe means Ascend training engineering know-how can be reused by other institutions — the standard playbook for ecosystem diffusion.

Combined with Huawei's disclosure that "CANN has entered routine open-source operations, with external developers at 61%", the Ascend ecosystem is shifting from "vendor-led" to "community-operated".

2. Inspur MetaBrain SD200 Ultra: Measured Throughput Claims for a 128-Chip Domestic Inference System​

Inspur launched the MetaBrain SD200 Ultra inference system around the same time:

MetricValue (company figures)
Domestic AI chips128 chips (vendor did not disclose specific models)
Model servedKimi K3
Token throughput2.8T tokens (system throughput figure under that methodology)
Latency5.85 ms

Three readings:

  • Hundred-chip-class system integration: 128 chips cooperating across a heterogeneous setup to run very large MoE inference is a test of interconnect topology, scheduling, and fault tolerance — OEMs are now demonstrably capable of assembling domestic chips into large systems;
  • Aimed at top open-source models: targeting Kimi K3 (already live on AWS Bedrock) as the benchmark workload shows the acceptance standard for domestic inference systems is "runs today's most popular models", not a self-referential demo;
  • Mind the methodology: the 2.8T token throughput and 5.85ms latency are both vendor claims; the tested model version, concurrency, and batch size were not disclosed, so procurement evaluations should re-test under real workloads.

3. EVAS Closes RMB 2 Billion B+ Round: The Cloud Compute Chip Race Still Attracts Capital​

EVAS (Yixing Intelligence) completed a B+ round of RMB 2 billion, at a post-money valuation near RMB 15 billion, with Oriza Capital following on; earlier in the first half of the year it had closed a RMB 1.5 billion Series B and brought in China Mobile as a strategic investor. The company focuses on next-generation cloud compute chips.

In 2026, when the flagship chip lineup (Ascend / Cambricon / Moore Threads / Hygon / Zhenwu) looks settled, a cloud compute chip newcomer still raising RMB 2 billion suggests:

  • Primary-market conviction in the "second tier of domestic compute": with top vendors' capacity booked into 2027, overflow demand gives newcomers a window;
  • The role of carrier strategic investment: China Mobile is both a buyer and an investor — the demand side of domestic compute is using capital to lock in the supply side;
  • Differentiated room remains for cloud inference DSA routes (see the ecosystem positioning of routes like Tsingmicro and Houmo).

4. Assembling the Week's Signals: The Loop Is Taking Shape​

Put the three signals together with this month's flagship launches, and every segment of the domestic compute loop now has players filling the gaps:

SegmentThis Month's Evidence
Flagship chipsAscend 960 early official unveiling (9-17), Zhenwu V900 launch (9-22)
Training validationChina Telecom's Xing4.0 end-to-end trained on Ascend and open-sourced, DeepSeek betting on Ascend training
Inference systemsInspur SD200 Ultra running Kimi K3 on 128 chips
Software ecosystemCANN routine open-sourcing, external developers at 61%
Capital supplyEVAS B+ round of RMB 2 billion (post-money near RMB 15 billion)
Demand sideDeepSeek's alignment, Kimi K3 live on overseas cloud platforms

Independent players are investing across all four segments — "chips, systems, models, capital" — the biggest difference from the "isolated breakthroughs" of 2024-2025.

5. Takeaways​

  • China Telecom Xing4.0-29B: the first open-source model end-to-end trained on Ascend outside the Huawei ecosystem;
  • Inspur SD200 Ultra: a 128-chip domestic inference system at 2.8T token throughput / 5.85ms (company figures, pending re-testing);
  • EVAS RMB 2 billion B+ round: the cloud compute chip second tier is still getting real money;
  • What to watch: Xing4.0's training cluster scale and MFU, third-party re-tests of the SD200 Ultra, and EVAS's tape-out cadence.

Further Reading​

References​

  • The GPU Daily (2026-09-24): China Telecom open-sources Xing4.0-29B; Inspur MetaBrain SD200 Ultra
  • Toutiao (2026-09-21): EVAS completes RMB 2 billion B+ round, post-money valuation near RMB 15 billion
  • CSDN AI Daily (2026-09-21): Huawei CANN enters routine open-source operations, OceanStor M900 launched

This article is compiled from public reports. Xing4.0 training details and SD200 Ultra performance figures are company statements; testing conditions are subject to subsequent disclosures.

Domestic Big Three 2026 H2: Localization Rate Crosses 40% Toward 60%, Ascend 960 Roadmap, MLU690 and S5000 Ecosystems Ramp Up

· 6 min read
Industry Research Team

In 2026, China's AI chip market landscape has shifted from "NVIDIA unipolar dominance" to "overseas vendors leading, domestic multi-route catch-up." According to industry research, China's overall AI accelerator market was ~4M units in 2025, of which 1.65M were domestic, with share first breaking 40%; as products iterate and fabs follow up, the localization rate is expected to rise to 60%-70% by 2027. This article focuses on the latest H2 2026 progress of Huawei Ascend, Cambricon, and Moore Threads — the domestic "Big Three."


1. Huawei Ascend: 950 Capacity Fully Booked, 960 Roadmap Unveiled​

Ascend's core advantage is "architecture + full-stack ecosystem synergy," with ~800K units shipped in 2025, capturing 50% of the total domestic vendor share. The product iteration cadence is clear:

TimeProductNote
2025 Q1Ascend 910CMain transitional model
2026 Q1Ascend 950PRInference flagship
2026 Q4 (planned)Ascend 950DTTraining flagship, drives domestic HBM iteration
2027-2028Ascend 960 / 970Roadmap products

950 series capacity has entered a "fully booked" state: 950PR entered mass production in April 2026; June monthly capacity jumped to 500K-600K units (nearly 10x MoM), with a full-year target of 1.2M units at 100% certainty; ByteDance locked in 350K units for $5.6B, while Tencent / Alibaba / Baidu combined locked in 400K units.

Ascend 960 roadmap specs (per roadmap disclosure):

MetricAscend 960
ArchitectureAscend 6th gen (Da Vinci v6)
FP8 compute~4 PFLOPS
Memory288GB
Memory bandwidth9.6 TB/s
Super-nodeAtlas 960 SuperPoD, 15,488 cards, Lingqu optical-electrical converged bus
Debut2027 Q4 (roadmap)

The previous-gen Ascend 384 super-node has cumulatively shipped over 750 sets, deployed across 20+ industries including internet, operators, finance, education, and healthcare — Huawei calls it "the only domestic super-node that has trained a SOTA model."


2. Cambricon MLU690: H2 Mass Production, Entering ByteDance Bidding Window​

Cambricon is the core domestic compute leader in the absence of an Ascend IPO, with the technology gap continuously narrowing:

  • Siyuan 590 (7nm): Performance equivalent to 80% of A100, already supports DeepSeek, continuously adapting to mainstream large models like Qwen 3 and GLM
  • Siyuan 690 series: Will enter mass production in H2 2026, expected to achieve order scale-up during ByteDance's H2 bidding window
  • Revenue certainty: Equity incentive targets show >100% revenue growth for the next 3 years: 2026 revenue target 13.5B RMB, 2027 27B RMB, 2028 60B RMB

Cambricon fully benefits from the industry dividend of "domestic CSP capex + full adaptation of domestic large models and domestic chips," making it the most direct elasticity play on rising localization rate.


3. Moore Threads MTT S5000: Full-Function GPU + Ecosystem Breakthrough​

Moore Threads takes a differentiated "full-function GPU" route, with the flagship MTT S5000 based on the 4th-gen "Pinghu" MUSA architecture:

MetricMTT S5000
Dense AI compute1000 TFLOPS
Memory80GB
Memory bandwidth1.6 TB/s
Inter-card interconnect784 GB/s
PrecisionFP8 to FP64 full precision (training + inference)
SecurityFirst batch to pass national "Safe and Reliable Evaluation" (Level I)

Its engineering capability is verified: the Kuae (KUAE) intelligent computing cluster based on S5000 achieves 95% training linear scaling efficiency, with compute efficiency loss within 5% at ten-thousand-card scale; supports checkpoint-resume training with effective training time ratio >90%; and has trained a MoE-236B base model with >25 trillion tokens of corpus from scratch.

The ecosystem is Moore Threads' deepest moat: MUSA has achieved 100% core math library compatibility, 3000+ PyTorch operator compatibility, covers 55 categories of core AI operators, has official vLLM and SGLang support, Day-0 adaptation of mainstream models, and 800K+ developers. Its PD heterogeneous-disaggregation solution achieves equivalent replacement of international high-end GPUs at a 2:1 ratio with S5000, significantly reducing inference cost.

The 5th-gen "Huagang" architecture (released 2025-12) supports FP4 to FP64 full precision, with 50% higher compute density and 10x better energy efficiency than the previous gen, supporting 100K+ card clusters; cumulative R&D investment in the "Huashan" (train-infer integrated) and "Lushan" (graphics rendering) new chips based on this architecture exceeds 900M RMB.


4. Software Ecosystem Decides: Day-0 Adaptation Becomes Routine​

Beyond hardware, software ecosystem realization is the watershed for domestic compute in 2026:

  • Huawei's CANN heterogeneous computing architecture and MindSeries suite are fully open-sourced, with the community incubating 67 projects, 12.44M+ lines of code, and 3,500+ monthly active developers
  • The "release-and-adapt" closed loop between domestic large models and domestic chips has basically formed: Tencent Hunyuan T3 (295B), DeepSeek-V4, and GLM-5.2 all completed Day-0 adaptation
  • 2026 is regarded as the "first year of domestic super-nodes"; Huatai Securities estimates China's super-node architecture market will reach 341.4B RMB by 2028, with a 2026-2028 CAGR of 194%

5. Industry Judgment: From "Can It Be Built" to "Can It Be Used Well"​

The domestic Big Three are converging along three paths:

  1. Huawei: Locks government/enterprise and internet big customers with super-node system-level capability + full-stack software
  2. Cambricon: Impacts the revenue inflection point by narrowing the training-side gap + scaling up via big-customer bidding
  3. Moore Threads: Covers cloud-edge-end full scenarios with full-function GPU generality + mature CUDA-compatible ecosystem

The common shortcoming of all three remains advanced process and HBM supply — precisely the core link of overseas controls. But as domestic HBM iterates and fabs follow up, a realistic path to 60%-70% localization by 2027 exists.

References​


This article is compiled from public industry research, broker views, and corporate announcements as of August 2026. Some shipment and market-share figures are third-party estimates, not officially confirmed data.

Huawei Ascend 950 Series Capacity & Orders Deep Dive: 950PR Monthly Capacity Jumps 10×, ByteDance Locks In 350k Units for $5.6B

· 4 min read
Industry Research Team

The Ascend 950 series (950PR inference / 950DT training) has become the core supply of domestic AI compute. Per multiple brokerages and industry research, 950 series capacity is 100% booked with scarce spot supply; the full-year 1.2M-unit target is "100% certain," with expectations of an upward revision to 1.5M. This article summarizes capacity and order data as of July 2026.

1. Capacity pace: ~10× MoM jump in June​

Time950PR monthly capacityNotes
May 202650k-60k unitsNear full production
June 2026500k-600k units~10× MoM; SMIC, Hua Hong tier-1 suppliers on overtime
Q3 2026 (est.)700k-800k unitsPer month
Full-year 2026 target1.2M unitsUpward revision to 1.5M expected

Supply chain delivery is tight: high-speed backplanes and liquid-cooling connectors' lead time stretched from 2 weeks to 6-8 weeks; orders are booked into 2027.

2. Order structure: top cloud providers + operators + overseas​

CustomerLocked volumeAmount / Notes
ByteDance350k 950PR$5.6B, concentrated delivery from Q3 2026
Tencent / Alibaba / Baidu~250k 950PR + 150k 950DTCombined ~400k units
Three major operators200k+ unitsCentralized procurement, for intelligent compute centers and AI private networks
OverseasSouth Korea 2,000 units, Malaysia 3,000 servers, Russia ten-thousand-card clusterFrom pilot to commercial

3. Shipment forecast: firmly #1 domestic​

Per CCA (Kezhi) Consulting estimates:

Metric20252026 (forecast)
Huawei Ascend total shipments812k cards1.026M cards
Of which 950PR—~800k units
Of which 950DT—~100k-200k units

Huawei has completed the product transition from the 910 series to the 950 series. The internet industry has become Ascend's largest application market; competitive advantage is extending from single-hardware performance to software ecosystem and system capabilities.

4. Going overseas: formal South Korea entry in Q4​

Per Korean media ETNews, Huawei plans Q4 2026 to formally enter the South Korean market with the Ascend series and Atlas 950 SuperPod:

  • Local distributor agreements signed; two channel partners including SK Shieldus selected
  • Main products: 950PR (mass-produced and delivered since April) and 950DT (launched Q4)
  • Official line: 950PR inference performance is 2.87× that of H20, priced at about 1/4 of it

5. WAIC 2026: 1024-card live debut confirmed​

At WAIC 2026 (July 17-20, Shanghai), Huawei's Atlas 950 SuperPoD live hardware made its first public appearance — a 16 compute-cabinet, 1,024 Ascend-card scale — and won the conference's top honor, the SAIL Award:

  • Core metrics: total compute 1 EFLOPS FP8 / 2 EFLOPS FP4, 256 TB globally unified memory addressing, Lingqu 2.0 interconnect, 3 μs ultra-low RTT latency
  • Full configuration: 128 compute cabinets + 32 interconnect cabinets = 160 cabinets, ~1000㎡ footprint, carrying 8,192 Ascend 950DT, planned Q4 2026 launch
  • Commercial foundation: previous-gen 384 SuperNode has cumulatively shipped 750+ units, deployed in 20+ industries
  • Software ecosystem: CANN fully open-sourced end of 2025; community incubated 67 projects, 12.44M+ lines of code, 3,500+ monthly active developers

WAIC's debut confirmed the 950 series' "SuperNode-first" product logic: beyond single-card compute, system-level effective compute (interconnect bandwidth + unified memory + low latency) is the key dimension for domestic compute to benchmark against international flagships.

Ascend roadmap recap​

ProductPositioningKey metrics (official roadmap)
950PRInference1 PFLOPS (FP8) / 2 PFLOPS (FP4), 2 TB/s interconnect
950DTTrainingSuperNode core, launched Q4
960Train/inference2 PFLOPS (FP8) / 4 PFLOPS
970Next-genIn planning

Industry interpretation​

  1. Domestic substitution moves from inference to training: 950PR (inference) ramps first, 950DT (training) follows in Q4, combined with the Atlas 950 SuperPoD ten-thousand-card interconnect — domestic compute now has the complete "training substitution" puzzle for the first time.
  2. Capacity is the biggest variable: order certainty is extremely high, but SMIC/Hua Hong advanced-process capacity, HBM supply, and advanced packaging remain ramp bottlenecks — the root of "scarce spot supply."
  3. Going overseas opens a second growth curve: bulk procurement from South Korea, Malaysia, Russia, and Latin America marks domestic compute's shift from "internal circulation" to "external circulation."

References​


Data in this article is based on official and major brokerage research; capacity/orders are dynamic figures and will be continuously updated.

China AI Chip Landscape 2025: Ascend, Cambricon, Hygon — Who Will Dominate?

· 5 min read
Industry Research Team

Escalating U.S. export controls are forcing China's AI chip industry to accelerate self-reliance. By 2025, the discussion around domestic Chinese AI chips has shifted from "are they usable?" to "which one should I choose?"

This article systematically reviews the major players, core products, and actual deployment status of domestic AI chips, helping developers and procurement decision-makers understand the competitive landscape.