vLLM tops the Artificial Analysis ranking by delivering the highest throughput for DeepSeek V3.2, Qwen 3.5 397B, and MiniMax‑M2.5 on identical NVIDIA Blackwell Ultra hardware, thanks to extensive kernel‑fusion optimizations that remain in the main branch.
The article dissects DeepSeek‑V4’s local deployment using vLLM, explaining the steep hardware requirements, the complex heterogeneous KV‑cache architecture, and the aggressive kernel‑fusion and multi‑stream optimizations that together make high‑context inference both memory‑intensive and engineering‑heavy.
This article explains why certain GPU performance gains require direct CUDA kernel edits and walks through fine‑grained techniques such as data‑layout restructuring, warp‑level primitives, tiled memory accesses, kernel fusion, and dynamic execution paths, backed by code examples and benchmark insights.
This article introduces OpenAI's Triton language, compares its block‑wise programming model to traditional CUDA, walks through vector‑addition and fused‑softmax kernel implementations, and presents benchmark results that demonstrate significant speedups over native PyTorch operations.
By fusing sparse‑feature operators, enabling multi‑stream execution, consolidating data copies, and merging inference batches, iQIYI reduced GPU CTR model latency to CPU‑level, boosted throughput over sixfold, and cut operational costs by more than 40%, overcoming launch‑overhead bottlenecks.
This article explains how PAI‑Blade uses compiler optimizations, TorchDynamo, MHLO conversion, and aggressive kernel fusion to accelerate PyTorch training, provides simple two‑line integration code, showcases benchmark results on A10 and A100 GPUs, and details deployment steps on PAI‑DSW.
