華泰證券:DeepSeek有望加速模型訓練與CUDA解耦
華泰證券研究認為,DeepSeek在V3中使用了相比CUDA更底層的PTX來優化硬件算法,PTX是CUDA編譯的中間代碼,在CUDA和最終機器碼之間起到橋樑作用。而NSA則使用了OpenAl提出的Triton編程語言高效編寫GPU代碼,Triton的底層可調用CUDA,也可調用其他GPU語言,包括AMD的rocm以及國產算力芯片語言,如寒武紀的思元590芯片和海光信息的深算一號(DCU)內置的HYGON ISA指令集。LLM的訓練短期內雖未完全脱離CUDA生態,但DeepSeek NSA的推出使其初步呈現出與CUDA解耦的趨勢,併為後續適配更多類型的算力芯片奠定基礎。以異騰為代表的國產算力已經很好的適配了DeepSeek-R1等國產模型,並取得了高效推理的效果,華泰證券認為,伴隨海外算力的受限,針對國產算力的優化或將有持續進展,值得重視。
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