ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration
arXiv — cs.AI · Anshu Raina, Yao Fu, Zhang Jiejing, Peyman Razaghi, Shiksha Patel, Ravi Gupta, Deval Shah, Adeem Jassani, Subhajit Dutta Chowdhury, Lorri Rao, Kailash Gogineni, Zhenyu Gu, Onil Gunawardana, Emad Barsoum · Published on Readzer
Summary
ROCm InferaSim predicts LLM serving performance from a few short measurements, reducing the need for exhaustive testing. On AMD Instinct MI355X GPUs with vLLM, SGLang, and ATOM, 11 anchors took 9.1 GPU-hours versus 204 GPU-hours for measuring 226 configurations, with median error of 8.1% on time per output token. For 159 disaggregated deployments, throughput predictions were within 5.5%, and a five-candidate short list contained a deployment within 12% of the best feasible pairing.
This is a reviewed summary. Read the complete article at the original source.
Why it matters by industry
- Artificial intelligence
This research addresses the practical challenge of optimizing LLM serving configurations, directly relevant to AI infrastructure and deployment efficiency.