vLLM Serving

• You need to serve LLM endpoints to multiple concurrent users and require high throughput (100+ req/sec).
• You want an OpenAI-compatible API so existing SDKs and tools work without code changes.
• You have large models (30B–70B+) and need quantization or tensor parallelism to fit them in available GPU memory.
• You are running batch inference jobs over large datasets and want automatic, efficient internal batching.

• You need CPU-only or edge/mobile inference — use llama.cpp instead.
• You are prototyping or running one-off experiments where raw throughput does not matter — HuggingFace Transformers is simpler.
• You require absolute maximum NVIDIA-specific performance and are willing to accept vendor lock-in — TensorRT-LLM may outperform vLLM on H100s.

Manages GPU KV cache in fixed-size memory blocks, eliminating fragmentation. This is the core innovation enabling up to 24x throughput gains over standard inference.

Launches a server that accepts the same request format as OpenAI's chat and completions endpoints. Any client using the OpenAI SDK can switch to vLLM by changing base_url to http://localhost:8000/v1.

Dynamically combines new incoming requests with in-progress ones, keeping GPU utilization consistently high across fluctuating traffic patterns.

Serves pre-quantized models with minimal accuracy loss. AWQ is recommended for 70B models, GPTQ offers broad model support, and FP8 delivers maximum speed on H100 GPUs.

Splits model layers across multiple GPUs using a power-of-2 shard count. Enables serving 70B+ models across 2–8 GPUs without custom sharding code.

Exposes vllm:time_to_first_token_seconds, cache utilization, and queue depth on a dedicated metrics port. Prefix caching reuses cached system-prompt KV states to reduce TTFT for repeated contexts.

Deploy a vllm serve instance behind a reverse proxy to power a customer-facing or internal chatbot. The OpenAI-compatible endpoint means zero changes to existing frontend code.

Pass thousands of prompts to llm.generate() in a single call. vLLM handles internal batching automatically, making it efficient for dataset annotation, evaluation pipelines, or bulk content generation.

Serve a 70B model on a single A100 80 GB or across two A100 40 GB GPUs using AWQ quantization and tensor parallelism, achieving production-grade throughput without expensive multi-node setups.

Containerize vLLM with Docker or Kubernetes and scale horizontally. The built-in metrics endpoint integrates directly with Grafana/Prometheus stacks for SLA monitoring.