Deepseek & Huawei Made Local AI Run Without NVIDIA's CUDA (And Why it's Hard)

Deepseek & Huawei Made Local AI Run Without NVIDIA's CUDA (And Why it's Hard)

Title history
  • Deepseek & Huawei Made Local AI Run Without NVIDIA's CUDA (And Why it's Hard) — observed on 20261031-2318 UTC (YouTube)

by Kai · View the channel on PSI

Formal & Physical Sciences AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 Kai 👥 65K 📅 October 4, 2026 ⏱ 16 min 👁 45K 📄 expert opinion 🧭 2026-10-31
Available in: English (current) Français

Keywords

DeepSeekHuawei AscendCUDAAI hardwarekernel libraries

Summary

The video analyzes DeepSeek’s release of six kernel libraries for Huawei Ascend chips, examining the viral claim of 99.8% utilization. It clarifies that this figure refers to software efficiency relative to the hardware ceiling, which is 432 TFLOPS for BF16, and compares it to Nvidia’s H100 at 1,979 TFLOPS. The video highlights that DeepSeek’s own FlashMLA benchmarks show a B200 outperforming the Ascend 950 by a factor of 3-4 for the same kernels, while acknowledging Huawei’s strengths in memory capacity (144 GB vs 80 GB). It corrects misconceptions about TileLang (not DeepSeek’s, merits based on Apache TVM) and CANN (Huawei’s CUDA counterpart). The DeepEP-Ascend repo is flagged for missing a license and targeting pre-production hardware. The video concludes that while the software gap is closing, the hardware gap remains significant, and the 950 chip is not yet available to customers. It offers a balanced perspective, noting Huawei’s openness in allowing external benchmarking and the strategic implications for Nvidia.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides substantial value by dissecting a widely circulated claim and grounding it in verifiable data from official repositories. It goes beyond headlines to explain what 99.8% actually means, using concrete teraflop comparisons and detailing the limitations of the release (pre-production hardware, missing license, incomplete distributed training features). The argumentation is logically structured and balanced: it acknowledges genuine achievements (e.g., high utilization, memory advantages) while exposing weaknesses (lower raw compute, ecosystem immaturity). The author supports each point with specific numbers and references, making the analysis both informative and credible. The reasoning is sound and avoids speculation, instead urging viewers to wait for independent benchmarks.

Scientific Rigor, Source Quality, Title Accuracy

The video demonstrates strong scientific rigor by citing specific data from DeepSeek’s own GitHub repositories (though URLs are not provided in the description) and by correcting factual errors found in other media coverage. It distinguishes between verified facts (e.g., the 432 TFLOPS ceiling, the license issue) and informed interpretation. The title is appropriate and does not overpromise, accurately reflecting the content’s focus on the challenges of replacing CUDA. The author also transparently discloses his previous incorrect prediction, enhancing trustworthiness. While the sources are primarily primary documents (repos) and the author’s own analysis, the lack of independent verification from third-party benchmarks is noted.

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Title / Content Match

The title accurately reflects the content: the video discusses DeepSeek's efforts to enable AI without Nvidia's CUDA via Huawei Ascend hardware, and explains the technical and practical challenges involved.

Quality & Reliability

8/10

The video provides a thorough, well-documented analysis of DeepSeek's kernel libraries for Huawei Ascend, based on official READMEs and benchmark tables. It corrects common misconceptions and offers a balanced view of hardware and software gaps. However, it relies on the author's interpretation of the data and does not provide independent verification.

Chapters

Cited Sources

Contribution & Novelties

This video contributes original analysis by deconstructing the 99.8% utilization claim and providing a nuanced comparison between Nvidia and Huawei hardware based on DeepSeek’s own published benchmarks. It clarifies the distinction between software efficiency and raw hardware performance, and identifies specific missing elements (e.g., license, support for older chips) that are often overlooked. The video also sets the record straight on misattributions like TileLang, demonstrating careful fact-checking.

Pour aller plus loin :

  • CUDA — Overview of Nvidia’s parallel computing platform, relevant to understanding the software stack discussed.
  • Apache TVM — An open-source machine learning compiler framework that underlies TileLang, helping to contextualize the compiler ecosystem.
  • Huawei Ascend — Background on Huawei’s AI chip series, including the 950, providing context on the hardware generation.

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Radar Profile

The radar profile shows strong scores in information quantity and quality, reflecting the video's detailed and accurate content. Technical level is high but accessible, while reliability is slightly lower due to reliance on the author's interpretation. Overall, a well-rounded analysis.

Reliability 8/10