The article analyzes why most open‑source large models cannot run on Huawei Ascend NPU, detailing the CUDA‑centric ecosystem, Ascend's CANN stack, three core technical hurdles, and the deep collaboration and tooling that enabled DeepSeek V4’s successful adaptation.
DeepSeek has released TileKernels, a GPU kernel library written in the TileLang DSL, that targets H100/H200/B200 GPUs and claims to approach hardware limits in compute intensity and memory bandwidth, offering MoE routing, FP8/FP4 quantization, and dual‑language PyTorch references for deep‑learning engineers.
This article surveys the 2025 AI infrastructure landscape, covering distributed inference with PD‑separation, dynamic DOPD scheduling, AFD attention‑FFN disaggregation, high‑bandwidth cross‑machine communication libraries, the TileLang programming model, RL train‑inference decoupling via SeamlessFlow, and secure, low‑latency agent infra designs for future large‑scale models.
