vLLM

High‑throughput LLM inference and serving engine

devtoolsadvancedFree tier

Overview

vLLM is a Python‑based inference and serving engine for large language models, designed for high throughput and memory efficiency.[33][39] A major release (v0.28.0) landed on August 26, 2026, with ongoing updates, and it is widely used to host open‑weight models in production.[33] Developers should care because vLLM offers a mature, actively maintained engine with features like paged attention, multi‑GPU support, and an ecosystem of integrations for deploying LLM APIs at scale.

Best For

+ml-engineers
+platform-engineers
+infra-teams

News & Updates

Running Llama 3.1 405B: A Practical Guide to Deploying Meta's Largest Open Model

A new technical guide details how to run Meta's Llama 3.1 405B, the largest open-source AI model with 405 billion parameters. The guide covers quantization techniques, hardware requirements, and inference optimizations, making it feasible for organizations with multi-GPU setups. It highlights using tools like vLLM and TensorRT-LLM to achieve real-time performance. This enables enterprises to deploy frontier-level AI on-premises, reducing reliance on closed APIs.

breakthroughs8/28/2026

LLMPanel Deploys vLLM to RunPod or Vast.ai Without Kubernetes

LLMPanel has introduced a new deployment method that allows users to run vLLM, a high-throughput inference engine, directly on GPU cloud platforms like RunPod and Vast.ai without needing Kubernetes. This simplifies the process of serving large language models by eliminating the complexity of container orchestration. The tool is designed for developers who want to quickly spin up vLLM instances on rented GPUs, reducing setup time from hours to minutes. This move lowers the barrier for deploying production-grade LLM inference, especially for small teams and individual developers who lack infrastructure expertise.

other8/25/2026