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About

vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more.

About

oMLX is a macOS-native MLX server designed to make local AI faster and more practical on Apple Silicon. Built for the way coding agents actually work, it uses paged SSD KV caching to persist cache blocks to disk, allowing previously seen prefixes to be restored across requests and server restarts instead of being recomputed from scratch. This can reduce time to first token on long contexts from 30–90 seconds to under five seconds after the first turn. Continuous batching handles concurrent requests through mlx-lm’s BatchGenerator, improving generation throughput without forcing requests to wait behind a single job. oMLX can serve LLMs, vision-language models, embedding models, and rerankers simultaneously, using LRU eviction when memory runs low. It supports any MLX-format model from Hugging Face, including Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can reuse models already stored in the standard Hugging Face cache, LM Studio folders, or custom directories.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

AI infrastructure engineers looking for a solution to optimize the deployment and serving of large-scale language models in production environments

Audience

Developers and AI power users needing to run fast local LLM inference and agentic coding workflows on Apple Silicon

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

vLLM
United States
vllm.ai

Company Information

oMLX
United States
omlx.ai/

Alternatives

Alternatives

Photon

Photon

Moondream
Run BiOS

Run BiOS

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OpenVINO

OpenVINO

Intel
BaseRT

BaseRT

Base Compute
Macyou

Macyou

Macyou LLC

Categories

Categories

Integrations

Hugging Face
OpenAI
Claude Code
Cursor
Database Mart
DeepSeek
GLM-4.1V
Gemma
Gemma
GitHub
JSON
KServe
Kubernetes
LM Studio
MiniMax
Mistral AI
Model Context Protocol (MCP)
OpenClaw
PyTorch
Python

Integrations

Hugging Face
OpenAI
Claude Code
Cursor
Database Mart
DeepSeek
GLM-4.1V
Gemma
Gemma
GitHub
JSON
KServe
Kubernetes
LM Studio
MiniMax
Mistral AI
Model Context Protocol (MCP)
OpenClaw
PyTorch
Python
Claim vLLM and update features and information
Claim vLLM and update features and information
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