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vLLM

High-throughput LLM serving engine

vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.

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About vLLM

vLLM is an open-source inference and serving engine for teams that want to run large language models behind production APIs. Its core architecture uses PagedAttention-style KV-cache management, continuous batching and related optimizations to improve GPU utilization for real online workloads rather than only offline benchmark scripts.

The project exposes OpenAI-compatible serving paths, structured-output controls, metrics, benchmarking tools and deployment guidance for Docker, Kubernetes and production networking. Current documentation also covers areas such as the OpenAI Responses API surface, tool-use examples, LoRA, quantization, multimodal models and integrations with frameworks including LangChain, LlamaIndex, Codex and Claude Code.

vLLM is a strong default for throughput-heavy self-hosted inference, but teams should avoid treating generic benchmark multipliers as procurement guarantees. Performance depends on the model, GPU, context length, quantization, parallelism and request mix, so production buyers should run their own tests before sizing hardware or promising latency targets.

Pricing & Platform Specs

Pricing Summary

vLLM is a 100% free and open-source LLM inference and serving engine released under the Apache 2.0 license ($0). There are no software licenses or subscription fees; operational costs depend solely on the user's underlying GPU compute and infrastructure.

Supported Platforms

Python, CUDA/accelerators, Docker, Kubernetes, OpenAI-compatible HTTP APIs

Explore categories, tags & use cases

Categories

Model Providers

Alternatives

All vLLM alternatives →
RunAnywhere SDK logo

RunAnywhere SDK

Cross-platform on-device AI inference SDK

RunAnywhere SDK is a production-ready toolkit for running AI models entirely on-device across iOS, macOS, Android, Web, React Native, and Flutter. It provides a unified C++ core with platform-specific bindings for LLM text generation via llama.cpp, vision-language models, Whisper speech-to-text, Piper text-to-speech, and on-device image generation. All processing stays local with zero cloud dependency, ensuring privacy and low latency for mobile and edge AI applications.

freemiumOpen Source
Triton Inference Server logo

Triton Inference Server

NVIDIA's optimized AI model serving platform

Triton Inference Server is NVIDIA's open-source inference serving platform that deploys AI models from TensorRT, PyTorch, ONNX, TensorFlow, OpenVINO, Python, and more across cloud, data center, and edge environments. It supports dynamic batching, model ensembles, concurrent model execution on GPUs and CPUs, and real-time, streaming, and batch inference patterns. Includes Model Analyzer for profiling and Model Navigator for automated optimization.

Open Source

Side-by-Side Comparisons

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vLLM
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NVIDIA logo
TensorRT-LLM

vLLM vs TensorRT-LLM: Open-Source Serving Flexibility or NVIDIA-Optimized Throughput?

vLLM and TensorRT-LLM both target high-throughput LLM inference, but they optimize for different teams. vLLM is the flexible open-source serving engine with broad model support, OpenAI-compatible APIs and a fast path from research to production. TensorRT-LLM is NVIDIA's GPU-optimized stack for teams willing to tune around NVIDIA hardware for maximum performance. Choose vLLM as the default serving layer; choose TensorRT-LLM when peak NVIDIA throughput matters more than portability.

vLLMTensorRT-LLM
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vLLM
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SGLang logo
SGLang

vLLM vs SGLang: Which Open-Source LLM Serving Engine Should You Use in Production?

vLLM and SGLang are two of the most important open-source LLM serving engines. Both support high-throughput inference, OpenAI-compatible APIs, structured outputs, batching, and production metrics. vLLM is the safer general-purpose default; SGLang is especially compelling for prefix-reuse-heavy, structured, and multi-call LLM applications.

vLLMSGLang
vLLM logo
vLLM
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SGLang
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Hugging Face logo
Text Generation Inference

vLLM vs SGLang vs TGI — Picking an Open-Source LLM Inference Server

If you are deploying a large language model to production, three open-source inference servers dominate the decision: vLLM, SGLang, and Hugging Face's Text Generation Inference (TGI). All three speak OpenAI-compatible HTTP, run continuous batching, and support tensor parallelism. The differences live in what they optimize for. vLLM is the incumbent — PagedAttention made it the default for most production deployments. SGLang is the challenger, leading on structured output and KV cache reuse through RadixAttention. TGI is the veteran: Hugging Face's own serving layer and the safest enterprise-Linux-plus-NVIDIA choice. This comparison covers architecture, benchmark context, model support, and team fit.

vLLMSGLangText Generation Inference
LoRAX logo
LoRAX
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vLLM logo
vLLM

LoRAX vs vLLM — Multi-LoRA Serving Platform vs High-Throughput LLM Inference Engine

LoRAX and vLLM both serve LLM inference workloads but optimize for different deployment scenarios. LoRAX specializes in serving hundreds of fine-tuned LoRA adapters from a single base model, enabling cost-effective multi-tenant model serving. vLLM provides the highest-throughput single-model inference through PagedAttention memory management, continuous batching, and speculative decoding optimizations.

LoRAXvLLM
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Community experience

Sources & verification

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Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.

FAQ

What is vLLM?

vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.

Is vLLM free?

Yes — vLLM is open source and free to use. vLLM is a 100% free and open-source LLM inference and serving engine released under the Apache 2.0 license ($0). There are no software licenses or subscription fees; operational costs depend solely on the user's underlying GPU compute and infrastructure.

Is vLLM open source?

Yes — vLLM is open source.

Is vLLM still maintained?

Yes — vLLM is active. Its listing was last verified on August 26, 2026.

What are the best vLLM alternatives?

The first editor-selected vLLM alternatives are RunAnywhere SDK, Triton Inference Server.

How does vLLM score in our review?

The published editorial review lists vLLM at 91/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.