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DeepSeek Is Trying to Make Nvidia Less Essential
DeepSeek is exploring another way to challenge Nvidia: making competing hardware easier to use, even without matching its chip performance.
On September 30, DeepSeek open-sourced programming infrastructure for Huawei’s Ascend platform, including computing and communication libraries. The companies also highlighted work on a supernode built around 128 Ascend 950 processors, according to Reuters.
The challenge is captured by a striking estimate from months earlier. At a May investor meeting, DeepSeek founder Liang Wenfeng said training a model comparable in scale to the leading US systems could require roughly 50,000 Nvidia GB300s or 200,000 Huawei Ascend 950s. The Information reported that it had confirmed the authenticity of the meeting transcript. These were Liang’s estimates of the resources needed for a training effort, not a controlled benchmark proving equivalent performance between the two platforms.
The number point to an awkward trade-off. Why invest in making a hardware platform easier to use if it might require four times as many processors?
The answer begins with what DeepSeek can actually get its hands on and leads to a part of Nvidia’s business that is much harder to see than a chip specification.
Why DeepSeek Needs Another Way to Compute
For an AI lab, computing capacity is useful only when it is available in sufficient quantities, at a workable cost, and in a form its engineers can use.
US export restrictions have limited Chinese companies’ access to advanced Nvidia processors. Domestic chips offer another route, but supply remains a constraint. In his May remarks, Liang described computing resources as the principal gap between Chinese AI labs and their US counterparts. He also acknowledged that Huawei’s production capacity was limited.
Nor is switching to domestic hardware an automatic way to cut costs. In September, Reuters reported that Huawei had raised the indicated price of its Ascend 950DT accelerator card to more than 250,000 yuan, up 20% to 50% from earlier quotes, as a global shortage of high-bandwidth memory pushed up costs for Chinese chipmakers.
The price of a processor is only part of the calculation. What matters is how much useful work a system can deliver, how much electricity it consumes, how reliably it runs, and how much engineering it takes to keep it productive.
DeepSeek is trying to make a constrained source of computing capacity more usable. Even if Huawei’s hardware requires more processors for a given training job, it becomes a more meaningful option if the software makes those processors easier to program and deploy.
A supply of chips that developers struggle to use is not much of an alternative. That brings the software stack into the centre of the story.
TileLang and the Cost of Switching
Nvidia’s advantage extends beyond its GPUs. CUDA has become embedded in the software, optimized libraries, tools and development habits that AI teams rely on. Years of work have accumulated around it. Moving to a different processor can mean rewriting kernels, adapting workflows and then tuning the results all over again.
DeepSeek’s work with TileLang targets part of that problem.
TileLang is an open-source, high-level programming language designed to make it easier to write high-performance kernels for different hardware platforms. TileLang gives developers a common programming layer for expressing operations, while the compiler handles the platform-specific work needed to target supported hardware.
DeepSeek’s TileKernels repository added Huawei Ascend support on September 30.
The project says its kernels now have a second backend, allowing the same Python APIs to run on Nvidia GPUs and Huawei NPUs.
The practical scope matters. TileKernels includes optimized operations used in AI training and inference, such as mixture-of-experts routing and quantization. These are pieces of the software that determine how efficiently a model runs. Developers can reuse parts of that work rather than rebuild every operation for a new platform.
TileLang does not make every CUDA program run on Ascend unchanged.Different processors still need different optimizations, and a portable programming interface cannot replace the entire ecosystem of libraries and production tools that has accumulated around Nvidia.
Still, the cost of switching need not remain as high as it once was. If developers can reuse more of their code and optimization work, testing an alternative becomes a more realistic option. For a company that cannot count on getting all the Nvidia hardware it needs, that is a practical gain.
What 128 Chips Have to Do with It
A supernode built around 128 Ascend processors brings a less visible challenge into focus: coordinating them efficiently.
Imagine a factory with...
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