Use case
Kernel developers running model training or inference on Ascend NPUs need core operations such as matrix multiplication to run fast enough with aligned precision so the overall training or inference job finishes in acceptable time.
Using the hardware vendor's kernel library, or writing and tuning matrix multiplication kernels in house.
High-performance kernels on Ascend have mainly come from the hardware vendor or in-house work, so model teams migrating often get stuck on kernel performance and precision with no reusable implementation available.
xOcto's call
Demand is evidenced
The trend is that model vendors are porting their own training kernels to domestic accelerators, showing that Ascend ecosystem software gaps are being filled by outside teams. An opening is kernel-level performance optimization and precision alignment services for Ascend users, delivered against verifiable speedup ratios.
Reason to use it
Why users would choose it
Inference: compared with in-house kernel development, this library open-sources the matrix multiplication implementation used in DeepSeek training onto the Ascend platform, so migrating teams can call it instead of rewriting, removing the step of kernel authoring and tuning; the real gain depends on covered shapes and precision, which are not yet verified.
Where the easy answer breaks down
The tension worth following
An English validation note will follow from the public evidence.
If this is your job
Worth trying. Inference: compared with in-house kernel development, this library open-sources the matrix multiplication implementation used in DeepSeek training onto the Ascend platform, so migrating teams can call it instead of rewriting, removing the step of kernel authoring and tuning; the real gain depends on covered shapes and precision, which are not yet verified.
Entry and what to borrow
The trend is that model vendors are porting their own training kernels to domestic accelerators, showing that Ascend ecosystem software gaps are being filled by outside teams. An opening is kernel-level performance optimization and precision alignment services for Ascend users, delivered against verifiable speedup ratios.