vLLM

vLLM is an inference and serving engine for large language models that provides high throughput and memory efficiency.

vLLM is an inference and serving engine designed for Large Language Models. It enables fast, cost-efficient model deployment and delivers high-throughput performance. The engine uses PagedAttention to manage memory efficiently and maximize GPU utilization through advanced scheduling and continuous batching. It supports a wide range of open-source models on various hardware platforms and includes a drop-in OpenAI-compatible API for straightforward integration. The project requires Python 3.10 or higher and offers stable and nightly installation options.

vLLM at a glance

Best for: Software engineer, Researcher
Runs on: self-hosted, open source, API, CLI
Pricing: Free
Released: February 2023
Category: AI coding tool
Website: vllm.ai

Key features

PagedAttention for memory efficiency
Continuous batching
OpenAI-compatible API
Support for open-source models
Advanced scheduling

Use it for

Serving large language models
Deploying open-source AI models
Running high-throughput inference

Alternatives to vLLM

AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
LightLLM — It delivers high-speed inference and serving performance for large language models.
WebLLM — Runs high-performance language models directly inside web browsers.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.
MLC LLM — Enables native optimization and deployment of AI models across platforms.
OpenLLM — It simplifies infrastructure management and optimizes inference speed and cost.
Mesh LLM — Run large local models across multiple machines through one OpenAI-compatible endpoint.
ZML — It enables Python-free inference while abstracting hardware complexities.
LMCache — It stores, compresses, searches, and reuses KV caches across clusters.
whichllm — Ranks models using real benchmarks and hardware fit instead of parameter count.
MLX LM — Runs LLMs and fine-tuning natively on Apple silicon hardware.

All vLLM alternatives