Investigating Low-Parallelism Inference Performance in vLLM
Published:Jan 5, 2026 17:03
•1 min read
•Zenn LLM
Analysis
This article delves into the performance bottlenecks of vLLM in low-parallelism scenarios, specifically comparing it to llama.cpp on AMD Ryzen AI Max+ 395. The use of PyTorch Profiler suggests a detailed investigation into the computational hotspots, which is crucial for optimizing vLLM for edge deployments or resource-constrained environments. The findings could inform future development efforts to improve vLLM's efficiency in such settings.
Key Takeaways
- •vLLM's performance is significantly lower than llama.cpp in low-parallelism requests.
- •PyTorch Profiler was used to identify performance bottlenecks in vLLM.
- •The investigation focuses on optimizing vLLM for resource-constrained environments.
Reference
“前回の記事ではAMD Ryzen AI Max+ 395でgpt-oss-20bをllama.cppとvLLMで推論させたときの性能と精度を評価した。”