serving-llms-vllm

Serve LLMs with an OpenAI-compatible API by starting the vLLM server, which exposes chat and completions endpoints at localhost:8000/v1 for interactive testing. It uses PagedAttention and continuous batching to maximize GPU utilization and reduce latency.

Improve LLM serving throughput and reduce latency by applying vLLM memory optimizations like PagedAttention, continuous batching, prefix caching, and chunked prefill. These features maximize GPU utilization, improve tokens per second, and lower time to first token (TTFT).

Yes, you can use tensor parallelism and quantization in vLLM to serve large models in memory-constrained deployments. vLLM supports AWQ, GPTQ, and FP8 quantization formats alongside tensor parallelism for practical scaling across multiple GPUs.

Yes, vLLM requires GPU support to run high-throughput LLM inference efficiently. The framework depends on Torch and Transformers, and uses GPU hardware acceleration to execute PagedAttention and continuous batching for production serving.

Yes, vLLM supports offline batch inference workflows alongside OpenAI-compatible serving. You can process large datasets offline using the same vLLM engine, applying continuous batching and prefix caching to maximize throughput for bulk generation tasks.

Configure max-model-len, concurrency, and prefix caching server settings in vLLM to satisfy throughput and time to first token (TTFT) targets. Tuning these production parameters optimizes memory allocation and handles concurrent requests efficiently.