serving-llms-vllm

To deploy an LLM inference server with an OpenAI-compatible API, use vLLM to serve your model with continuous batching and paged KV cache management, exposing standard Chat Completions endpoints for multi-user chat workloads.

Continuous batching and PagedAttention improve LLM serving throughput by dynamically managing the paged KV cache during inference, which significantly increases tokens per second and reduces time-to-first-token under multi-user load.

You can serve large models like a 70B on limited VRAM by enabling quantization formats such as AWQ, GPTQ, or FP8, and applying tensor parallelism to distribute the inference workload across multiple CUDA-enabled GPUs.

The best way to reduce latency for multi-user chat workloads in LLM serving is to launch a vLLM server with continuous batching, PagedAttention for KV cache management, and appropriate memory utilization flags to maintain predictable latency.

Yes, you need CUDA-enabled PyTorch to run a vLLM inference server, as the framework requires GPU acceleration to execute high-throughput LLM serving, manage the paged KV cache, and support tensor parallelism.

While quantization enables deploying larger LLMs on memory-constrained hardware by reducing VRAM usage, it requires compatible model formats like AWQ, GPTQ, or FP8 and may necessitate tensor parallelism to maintain high throughput and low latency.