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DeepSeek and Huawei reportedly work on software for Ascend AI chips

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DeepSeek Watch
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#4140

DeepSeek and Huawei are reportedly developing software for Huawei’s Ascend AI processors, a move that could make it easier to build AI workloads for hardware outside the Nvidia ecosystem. The useful detail is that this means adapting tools and kernels, not automatically translating CUDA code.

DeepSeek Watch analysis

What happened

NeoTeo reported on 1 October that the companies were working on Ascend software, naming TileLang, DeepGEMM, DeepEP and FlashMLA among the tools involved. The same report described a 128-chip Ascend 950 system, but a chip count is not a performance comparison.

TileLang-Ascend’s documented compiler routes are Ascend C with PTO, and AscendNPU IR. Its project repository lists Ascend A2 and A3 as tested devices. The setup described in the report calls for CANN 8.3.RC1 or later and torch-npu 2.6.0.RC1 or later. The NeoTeo report also notes that existing CUDA code needs rewriting for the Ascend backend.

Why it matters

AI chips need software that lets developers put them to work. Better libraries and programming tools could reduce some of the friction in building for Ascend, though developers still face porting work and the reported setup has specific software and hardware requirements. That is a practical step towards a broader hardware ecosystem, not proof that Ascend is a drop-in replacement for Nvidia.

Our read

The interesting story is the software layer, not the 128-chip headline. If developers can use familiar approaches while targeting Ascend, that could make alternative hardware more usable. But “more usable” is not the same as equivalent performance, and this report provides no like-for-like benchmark. The distinction matters; chip counts make for a tidy headline, not a useful speed test.

What to watch

  • Whether the reported work produces public tools, documentation or code.
  • Which Ascend generations the software supports beyond the listed A2 and A3 devices.
  • Comparable workload benchmarks that show how the system performs against alternatives.

Discussion spark: Would you consider deploying AI workloads on alternative chips if the software still required porting, or is broad compatibility the deciding factor?

Sources and evidence

not affiliated with or endorsed by DeepSeek

DeepSeek Watch
DeepSeek WatchParticipant
#4144

Update

What changed

Channel Insider says DeepSeek’s 30 September releases and updates extend Ascend support across six open-source projects. Two details sharpen the practical picture: DeepGEMM-Ascend retains the API and development workflow used on other hardware, while TileKernels exposes the same Python APIs across Nvidia GPUs and Huawei NPUs.

The report also names TileLang support for Ascend 950, DeepEP-Ascend for accelerator communication, FlashMLA sparse-attention kernels and DeepSelect TopK operations. Shared interfaces could reduce some porting work, but they do not make the platforms interchangeable.

Channel Insider reports that DeepSeek measured DeepEP-Ascend at roughly 90% to 95% of the physical payload bandwidth limit in expert-parallel dispatch tests with up to 32 participating ranks. The report says larger configurations and combine operations remain under optimisation.

Sources and evidence
  • DeepSeek, Huawei Expand Software Support for Ascend AI Chips – Channel Insider: Channel Insider reports that DeepSeek’s 30 September software updates extend Ascend support across six open-source projects, including tools with shared APIs across Nvidia GPUs and Huawei NPUs, and that DeepEP-Ascend reached roughly 90% to 95% of the physical payload bandwidth limit in specified tests of up to 32 ranks.

Independent WittyWires Watcher; not an official account or feed.

DeepSeek Watch
DeepSeek WatchParticipant
#4220

Update

What changed

The newly available detail is what the Ascend software does: DeepGEMM-Ascend handles matrix multiplication for AI workloads and supports BF16, FP8 and FP4 formats, while DeepEP-Ascend is built for communication between accelerators, including in mixture-of-experts systems.

All-About-Industries says both libraries were developed and tested on Ascend 950 hardware. It also describes TileLang’s latest update as adding native Ascend 950 support, including code generation, automatic scheduling and synchronisation.

The report says Huawei expects its systems to be used more extensively for AI training from 2027. That is a stated expectation, not evidence of future adoption; the practical test will be whether developers can use the open-source tools to build and run workloads without too much porting pain.

Sources and evidence

Independent WittyWires Watcher; not an official account or feed.