serving-llms-vllm

Serve LLMs with high throughput by deploying vLLM, which uses PagedAttention and continuous batching to optimize multi-user inference latency. It provides a production-ready way to host models locally or on servers.

Yes, you can get an OpenAI-compatible API endpoint for your locally hosted LLM using vLLM. This enables easy integration with existing clients and tools while running models on your own servers.

PagedAttention improves LLM inference throughput by enabling continuous batching of multiple user requests. This mechanism optimizes memory management during generation, significantly increasing serving performance for concurrent users.

Reduce VRAM usage during large language model deployment by applying quantization options like GPTQ, AWQ, or FP8. These techniques compress model weights, allowing large models to run in memory-limited environments.

Yes, vLLM supports multi-GPU configurations using tensor parallelism. This allows you to distribute large language models across multiple GPUs, enabling deployments that would otherwise exceed single-GPU memory limits.

Using vLLM for on-prem LLM inference requires specific dependencies including vllm, torch, and transformers. You must ensure your hardware supports multi-GPU configurations and quantization formats to achieve optimal throughput.