RealFeedApp Flame is 0.55% slower than standalone vLLM
Persistent worker placement improved cache reuse in both standalone and Flame systems. Larger batches and asynchronous engine admission met the target of at least 99% reference throughput.
- Field
- applying language models
- What they did
- Compared standalone vLLM with the Flame Python service that embeds vLLM, finding that Flame's median completion throughput was 0.55% to 0.86% lower.
- Why it matters
- This helps understand how effectively language models can be deployed in standalone services to avoid rebuilding data when handling repeated requests.
#vllm#flame#inference#gpu#llm
