inference-serving-vllm

Topics in the source

• vLLM - High-Performance LLM Serving
• Quick start
• Common workflows
• Workflow 1: Production API deployment
• Workflow 2: Offline batch inference
• Workflow 3: Quantized model serving
• When to use vs alternatives
• Common issues

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: 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) 100 req/sec. Step 3: Enable monitoring vLLM exposes Prometheus metrics on port 9090: Key metrics to monitor: vllm:timetofirsttokenseconds - Latency vllm:numrequestsrunning - Active requests vllm:gpucacheusageperc - KV cache utilization Step 4: Deploy to production Use Docker for consistent deployment: Step 5: Verify performance metrics Check that deployment meets targets: TTFT target req/sec GPU utilization > 80% No OOM errors in logs For processing large datasets without server overhead. Copy this checklist: Step 1: Prepare input data Step 2: Configure LLM engine Step 3: Run batch inference vLLM automatically batches requests for efficiency: Step 4: Process results Fit large models in limited GPU memory. Step 1: Choose quantization met

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