Serving Llms Vllm
원문: serving-llms-vllm
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and
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vLLM High Performance LLM Serving Quick start vLLM achieves 24x higher throughput than standard transformers through PagedAttention (block based KV cache) and continuous batching (mixing prefill/decode requests). Installation : Basic offline inference : OpenAI compatible server : Common workflows Workflow 1: Production API deployment Copy this checklist and track progress: Step 1: Configure server settings Choose configuration based on your model size: Step 2: Test with limited traffic Run load test before production: Verify TTFT (time to first token) < 500ms and throughput 100 req/sec. Step 3: Enable monitoring vLLM exposes Prometheus metrics on port 9090: Key metrics to monitor: vllm:time …
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