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DeepSeek’s reported move to optimize newer models for Huawei Ascend processors is a genuine strategic challenge to Nvidia, but it is not proof that Nvidia has been displaced worldwide. The clearest change is inside China: export controls and procurement policy are helping Huawei build a domestic hardware-and-software alternative, while DeepSeek supplies the high-profile workload validation that alternative needs.
The short answer
DeepSeek is reportedly using Huawei hardware for parts of its training, post-training, inference and software-optimization work. Reuters reported a Huawei-adapted model preview in April 2026, and later reporting linked DeepSeek V4 with Huawei’s newer Ascend platform. Chinese technology companies were then reported to be seeking additional Ascend capacity.
That does not establish that DeepSeek has abandoned Nvidia, or that every stage of V4 was performed on Huawei chips. A separate Reuters report cited a senior U.S. official alleging that DeepSeek’s latest model had been trained on Nvidia Blackwell processors in China. The public record therefore supports partial decoupling, not a complete hardware switch.
The immediate competitive impact is concentrated in China. Nvidia remains difficult to dislodge globally because of CUDA, mature libraries, networking, cloud availability, developer adoption and production-scale supply.
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What “a shift to Huawei chips” can mean
The phrase covers several technically different changes. They should not be treated as equivalent:
- Training: pre-training or continued training a model on Ascend processors.
- Post-training: reinforcement learning, fine-tuning, preference optimization or other refinement stages.
- Inference: serving the finished model to users and API customers.
- Software porting: adapting kernels, operators, compilers and distributed-training code to Huawei’s CANN and Ascend stack.
- Hardware-model co-design: changing parallelism, memory use, quantization, communication patterns or expert routing for Ascend.
- Commercial access: giving Huawei or other domestic suppliers early optimization access while limiting access for Nvidia or AMD.
Inference is generally easier to port than frontier-scale pre-training. A model can also be optimized for Ascend while remaining portable to Nvidia, and a Huawei deployment can use checkpoints originally produced on Nvidia hardware.
What DeepSeek reportedly changed
Testing several domestic accelerators
The Information reported that DeepSeek tested chips from Huawei, Baidu and Cambricon before selecting Huawei for work on Ascend-based training and refinement of smaller next-generation models: The Information. Testing does not prove that each supplier reached production deployment or equal performance.
A Huawei-adapted model
Reuters reported that DeepSeek previewed a new model adapted for Huawei technology on April 24, 2026, describing Ascend as China’s leading domestic alternative to Nvidia: Reuters report. The report was based on sources familiar with the matter, not a public independent hardware audit.
Demand after DeepSeek V4
Reuters later reported that major Chinese technology companies sought Huawei Ascend 950 capacity after DeepSeek V4 launched: Reuters report. Expressions of interest, purchase orders, shipments and deployed capacity are different things, so the report should not be read as proof that all of those chips were delivered.
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The evidence is real—and still incomplete
Evidence for Huawei optimization
- Reuters reported a model adapted for Huawei chips.
- Reporting described cooperation between DeepSeek and Huawei engineers.
- Chinese firms were reported to be seeking Ascend systems after V4.
Evidence Nvidia may still be involved
A Reuters report cited a senior U.S. official alleging that DeepSeek’s latest model was trained on Nvidia Blackwell chips in China: Reuters report. That is an allegation, not a final legal determination or a complete independent audit. Earlier reporting also said DeepSeek had withheld its newest model from Nvidia and AMD for performance optimization: Reuters report.
The accurate formulation is: DeepSeek is reportedly optimizing and deploying parts of its newer model stack on Huawei Ascend hardware, but public reporting does not establish that Nvidia has been removed from every stage of training or development.
Why DeepSeek matters to Huawei more than an ordinary customer
DeepSeek is a model developer whose architecture and efficiency techniques can influence the wider market. Its Ascend work can provide:
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- a demanding reference workload for Chinese accelerators;
- real-world targets for compilers, kernels and distributed-training tools;
- a model that Chinese cloud providers can expose through domestic APIs;
- evidence to other companies that Nvidia dependence can be reduced;
- engineering feedback on memory, networking, quantization and expert routing.
This is a software and workload-validation contest as much as a silicon contest. A successful DeepSeek deployment can stimulate Huawei demand even if an individual Ascend processor trails an Nvidia product in some tests.
How serious is the threat to Nvidia?
China revenue and market access
This is the most immediate risk. Nvidia Chief Executive Jensen Huang has said the company previously held about 95% of China’s AI-chip market, a figure reported by the Associated Press rather than an independently cited market-share dataset: Associated Press. Export controls have reduced Nvidia’s access to China while domestic policy favors Huawei and other local suppliers.
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The developer ecosystem
Nvidia’s moat includes CUDA, optimized libraries, framework integrations, debugging and profiling tools, cloud access, trained engineers and deployment experience. DeepSeek optimization for Ascend helps Huawei attack that moat by supplying a practical reference implementation rather than a laboratory specification.
Hardware and system performance
A CSIS analysis estimated that an Ascend 910C delivered roughly 60% of Nvidia H100 inference performance in an earlier comparison: CSIS analysis. That is workload- and configuration-dependent, not a universal chip ranking. Results vary with precision, batch size, model, memory, networking and software versions.
Global share
Nothing in the cited evidence shows Huawei displacing Nvidia globally. Huawei’s strongest position is China, where export restrictions and procurement rules change the competitive conditions. Outside that market, Nvidia retains broad availability, software compatibility, networking scale and established customer code.
Why export controls can accelerate Huawei
Export controls restrict Chinese access to Nvidia’s newest processors. That can give Chinese labs stronger incentives to port models to domestic hardware, while government and enterprise procurement creates demand Huawei can use to fund software and systems development. Huawei can coordinate accelerators, servers, networking, software and cloud services as one national stack.
This is a strategic inference from the observed market pattern, not a separately measured causal result. It also creates a risk for Nvidia beyond lost chip sales: fewer Chinese developers may build their next workloads around CUDA.
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Why Huawei is not automatically equivalent to Nvidia
- Manufacturing: advanced accelerators require sophisticated fabrication, packaging, high-bandwidth memory and systems integration.
- Supply: a successful demonstration does not prove hyperscale volume.
- Interconnect: large mixture-of-experts models depend heavily on communication bandwidth and latency.
- Software maturity: moving CUDA workloads to CANN and Ascend can require substantial rewriting and testing.
- Compatibility: existing libraries, model tooling and third-party services are heavily optimized for Nvidia.
- Benchmark quality: vendor tests may use different model versions, precision, sequence lengths, batch sizes and measurement methods.
- Operations: power, cooling, networking, staffing, failure recovery and utilization matter alongside accelerator prices.
Huawei’s CloudMatrix384 paper describes a system using 384 Ascend 910C NPUs and 192 Kunpeng CPUs and reports DeepSeek-R1 inference results: arXiv paper. Those are paper results from a Huawei-affiliated system and require independent comparison before being treated as a neutral benchmark.
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Huawei has also said Ascend 950DT will be available in the fourth quarter of 2026: Huawei roadmap statement. Roadmap timing does not by itself establish production volume, geographic availability or customer qualification.
Nvidia’s counterargument
Huang has argued that excluding Nvidia from China can strengthen Huawei by pushing Chinese developers toward its ecosystem. Nvidia’s position is that continued access to its products can slow domestic competitors’ progress. That is an executive’s strategic argument, not neutral evidence, but it identifies the central risk: losing China’s model-optimization feedback loop.
The South China Morning Post reported Huang warning that a DeepSeek-Huawei alignment would be damaging for the United States: SCMP report.
How to judge whether Huawei becomes a durable Nvidia challenger
- Can Ascend run the model at production scale, not just in a demonstration?
- Does it support both training and inference, or only inference?
- What is the total cost per useful output token after power, networking and staffing?
- How much CUDA code must be rewritten?
- Can Huawei supply enough chips, memory and complete systems?
- Are results reproducible outside Huawei-controlled infrastructure?
- Can multiple domestic chips run the workload, or is it tied to one optimized configuration?
- Can Chinese cloud providers expose the capability reliably to developers?
- Does the hardware attract a lasting developer ecosystem?
- Can Huawei compete outside China despite sanctions, export restrictions and support uncertainty?
China and the rest of the world are different markets
For mainland Chinese buyers, Huawei offers policy alignment, domestic supply-chain resilience and an Ascend-native path. For organizations in the United States, Europe and other markets with broad Nvidia access, switching also means migration costs and potentially narrower tooling and cloud choices.
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Hong Kong and international cloud regions require separate checks for account eligibility, data handling, model availability and cross-border latency. A Chinese deployment result should not automatically be generalized to every region.
Commercial implications for AI buyers
| Buyer need | Most relevant option | Main trade-off |
|---|---|---|
| Fast DeepSeek experimentation | DeepSeek official API | Usage pricing is volatile and may not meet data-residency or reliability requirements. |
| China-local deployment | Huawei Cloud MaaS and Ascend services | Region eligibility, onboarding and API compatibility must be verified. |
| Existing CUDA enterprise stack | Nvidia AI Enterprise and Nvidia-based clouds | Higher geopolitical exposure for restricted Chinese users. |
| Domestic procurement or sovereignty | Huawei Ascend ecosystem | Migration, supply and support risks remain. |
| International portability | Nvidia public-cloud infrastructure | Access and price depend on region and capacity. |
DeepSeek’s official API page lists V4-Flash and V4-Pro with 1-million-token context and states that prices may change: DeepSeek pricing documentation. The page displayed, on August 18, 2026, V4-Flash at $0.0028 per million cached input tokens, $0.14 per million uncached input tokens and $0.28 per million output tokens; V4-Pro at $0.003625, $0.435 and $0.87 respectively. These are volatile API prices, not proof of underlying training or inference cost.
Nvidia documents subscription, cloud-marketplace consumption and perpetual-license options for AI Enterprise: Nvidia licensing guide. Cloud pricing is generally quoted per GPU-hour through marketplaces or private offers, so it cannot be compared directly with token pricing without a workload model.
What to watch next
- Actual Ascend 950 shipment and deployment volumes.
- Public DeepSeek technical documentation identifying which pipeline stages use Huawei.
- Independent benchmarks reporting throughput, latency, power and total cost.
- Chinese cloud availability outside Huawei-controlled demonstrations.
- CUDA-to-CANN portability tools and the engineering effort they require.
- Nvidia’s China-specific product and partnership strategy.
- Government procurement lists and evidence of deployments outside China.
Bottom line
DeepSeek is helping Huawei demonstrate that domestic accelerators can support serious, frontier-model workloads. That matters because it can convert a policy-driven substitute into a usable ecosystem and erode Nvidia’s software influence in China. It does not yet show that Huawei has matched Nvidia’s global performance, supply, software maturity or market reach. The likely near-term outcome is a split market: Nvidia remains the global default, while Huawei becomes an increasingly important default stack for Chinese AI.
Frequently Asked Questions
Has DeepSeek completely stopped using Nvidia chips?
No. Public reporting supports Huawei optimization and deployment for parts of the newer stack, but it does not establish that Nvidia hardware was removed from every training or development stage.
Does DeepSeek’s Huawei work prove Huawei chips are faster than Nvidia’s?
No. Available comparisons are workload-specific, and Huawei-authored system results are not independent universal benchmarks.
Is this mainly a China or a global Nvidia threat?
The immediate effect is mainly in China, where export controls and procurement policy favor domestic alternatives. Global displacement is not established by the available evidence.
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