serving-llms-vllm

Serve large language models with high throughput using vLLM, which leverages PagedAttention and continuous batching to maximize GPU utilization for production APIs. It enables low-latency serving even under heavy concurrent request loads.

PagedAttention improves LLM inference by enabling efficient memory usage for very large models, while continuous batching maximizes GPU utilization by dynamically managing many simultaneous requests. Together they significantly increase serving throughput and reduce latency.

Deploy massive LLMs on GPU-memory-constrained hardware by applying quantization and tensor parallelism within vLLM. These features distribute the model weight and reduce memory footprint, allowing large models to run on limited GPU resources.

vLLM provides an OpenAI-compatible API that lets existing applications query the served model without requiring code changes. You can deploy the model via the vllm serve command and immediately query it using any OpenAI-compatible client.

To set up an OpenAI-compatible endpoint for code generation services, you need vLLM, PyTorch, and appropriate GPU resources. After running vllm serve with your model, you can query the API for fast response times in AI-powered applications.

Use vLLM when you need production-grade serving of large language models demanding high throughput and low latency. It is ideal for workloads like chat assistants or code generation APIs where maximizing GPU utilization and efficient memory usage are critical.