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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11DeepSeek and Huawei have released open-source software components aimed at making AI development on Huawei Ascend accelerators more accessible. The September 30, 2026 announcement adds Ascend-focused compute and communication libraries and native Ascend 950 support in TileLang. It expands the software available to developers; it does not establish that DeepSeek has replaced Nvidia CUDA or moved all of its development off Nvidia hardware.
What did DeepSeek and Huawei release?
The announcement covers three pieces of software for different parts of AI workloads: matrix computation, communication between accelerators, and kernel programming. They are developer infrastructure for Huawei’s Ascend platform, not a new consumer product. Reuters reported the announcement on September 30, 2026, and Tom’s Hardware described the libraries and TileLang update the following day.
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DeepGEMM-Ascend: computation
Tom’s Hardware describes DeepGEMM-Ascend as a library for matrix multiplication and other calculations used in DeepSeek models. The report says it supports BF16, FP8, and FP4 formats and is compatible with existing DeepGEMM programming interfaces. Those format and interface details are reported by Tom’s Hardware, rather than independently established here. Tom’s Hardware, October 1, 2026.
DeepEP-Ascend: accelerator communication
DeepEP-Ascend is DeepSeek’s communication library for training and inference on Ascend NPUs. Its repository describes expert-parallel all-to-all operations for dispatching inputs to experts in mixture-of-experts models and combining their outputs. It also lists pipeline, context/data-parallel, and remote-memory-access primitives as work in progress. The public buffer APIs align with NVIDIA DeepEP’s EPBuffer-based V2.5 APIs, but supported modes and stream behavior are Ascend-specific. DeepSeek, DeepEP-Ascend README.
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TileLang: kernel programming
TileLang is the higher-level kernel programming layer in the release. Tom’s Hardware says the September 30 update added native code generation for Ascend 950, along with automatic scheduling and synchronization. DeepSeek told Reuters it wants a high-level language that is broadly usable and still capable of accessing hardware performance; it described TileLang as offering a “simpler programming model” than CUDA. That is DeepSeek’s positioning, not independent evidence that TileLang is faster, easier for every developer, or a replacement for CUDA’s mature ecosystem. Reuters, republished by Investing.com, September 30, 2026.
Can the tools run on Huawei Ascend 950?
TileLang’s native Ascend 950 support is part of the announcement. DeepEP-Ascend’s published measurements, however, document a specific Ascend 950DT proof-of-concept environment, not broad compatibility with every Ascend chip or software combination.
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The DeepEP-Ascend README gives this benchmark configuration: Ascend 950DT, CANN 9.2.0, Python 3.12, PyTorch 2.13.0+cpu, torch_npu 2.13.0rc1, and a manually configured proof-of-concept HDK supplied to DeepSeek. The project cautions that its measurements were collected on that setup, not the planned commercial HDK, and that kernel support on other Ascend generations or CANN versions has not been established by those results. DeepSeek, DeepEP-Ascend README, accessed October 3, 2026.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAt that date, the README said Huawei’s Atlas 850E Q3 commercial HDK—recommended there for full-bandwidth operation—was planned for public availability around October 15, 2026, through Huawei’s software download page, subject to Huawei’s publication schedule. October 15 was still in the future on October 3, so this was a planned date, not confirmation of a release.
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What does DeepEP-Ascend’s benchmark show?
DeepSeek reports communication bandwidth results for a workload with 16,384 tokens per rank, hidden size 7,168, top-6 routing across 256 experts, and 10 warmups and 50 samples per rank. These are project-reported measurements from the proof-of-concept setup, not an independent test or a guarantee for commercial deployments.
| Expert-parallel size | Dispatch bandwidth | Combine bandwidth |
|---|---|---|
| EP8 | 373–375 GB/s | 345–347 GB/s |
| EP16 | 348–352 GB/s | 338–341 GB/s |
| EP32 | 335–340 GB/s | 320–324 GB/s |
| EP64 | 323–327 GB/s | 294–298 GB/s |
| EP128 | 313–320 GB/s | 272–278 GB/s |
DeepSeek says dispatch reaches roughly 90–95% of the physical payload-bandwidth limit for EP sizes up to 32. The README says larger EP sizes and combine remain under optimization; combine also faces local reduction overhead and HBM contention with URMA. The results therefore describe one workload and configuration, rather than a cross-platform performance ranking.
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How complete is the Ascend software support?
The repository labels PP, Engram, and Bucket interfaces experimental. It says Ascend reduce-scatter and all-reduce kernels are still being built, and expert load-balancing communication kernels have not been implemented. Hybrid communication, CPU-backed Engram storage, and graph capture are listed as unsupported. These gaps matter because “support” for the platform does not mean every parallelism mode or deployment path is ready for production.
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The tools are part of Huawei’s Ascend software stack, which uses CANN. Huawei calls CANN the foundation of the Ascend ecosystem. At a September 2025 keynote, Huawei said it planned to open-source CANN compiler and virtual-instruction-set interfaces, other CANN software, and Mind toolchains by December 31, 2025. That keynote records an announced plan at the time; it does not establish the present status of every component. Huawei, September 2025.
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Does this replace Nvidia CUDA?
No such conclusion follows from the release. The announcement adds Ascend-targeted compute and communication libraries and a kernel-programming option; the available evidence does not show that DeepSeek has shifted all development away from Nvidia hardware, that Ascend has feature parity with CUDA, or that either platform is faster on equivalent workloads.
A meaningful comparison would need to establish compatible hardware and software versions, operator and interface completeness, equivalent compute and communication benchmarks, developer migration effort, and access to supported hardware, firmware, and documentation. The published DeepEP results do not provide that controlled comparison.
What does Huawei report about the broader CANN ecosystem?
Huawei’s September 17, 2026 keynote says Ascend supports more than 90 leading third-party open-source projects, including PyTorch, Triton, vLLM, and veRL. Huawei also reported more than 5,200 monthly active developers in the CANN community and said external developers made up 61% of CANN developers. These are company-reported figures and provide ecosystem context; they are not independent adoption estimates or evidence of uptake for DeepSeek’s new tools. Huawei, September 17, 2026.
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