Audience
AI infrastructure engineers looking for a solution to optimize the deployment and serving of large-scale language models in production environments
About vLLM
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.
Other Popular Alternatives & Related Software
Ollama
Ollama is an innovative platform that focuses on providing AI-powered tools and services, designed to make it easier for users to interact with and build AI-driven applications. Run AI models locally. By offering a range of solutions, including natural language processing models and customizable AI features, Ollama empowers developers, businesses, and organizations to integrate advanced machine learning technologies into their workflows. With an emphasis on usability and accessibility, Ollama strives to simplify the process of working with AI, making it an appealing option for those looking to harness the potential of artificial intelligence in their projects.
Learn more about Ollama
Kimi K2.7 Code
Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.
Learn more about Kimi K2.7 Code
DeepInfer
DeepInfer is an open-source inference framework for running DeepSeek language models on Linux systems with supported NVIDIA or Huawei Ascend accelerators. It provides offline inference and runtime components for model execution, optimized kernels, distributed parallelism, asynchronous batch scheduling, speculative decoding, deterministic generation, and structured outputs such as JSON Schema, regular expressions, and EBNF formats. The project documents DeepSeek-V4.1-Flash support and includes build instructions, configuration guidance, examples, and evaluation tools. It is intended for developers and infrastructure teams building or evaluating self-managed inference deployments. Its repository notes that production services may require additional KV-cache, prefill/decode disaggregation, and load-balancing components not included in this release.
Learn more about DeepInfer
LocalAI
LocalAI is a free, open source, local-first AI platform designed as a drop-in replacement for the OpenAI API, allowing developers to run large language models and other AI systems entirely on their own hardware without relying on cloud services. It provides a complete AI stack for local inferencing, enabling text generation, image creation with diffusion models, audio transcription and speech synthesis, embeddings for semantic search, and multimodal capabilities such as vision analysis. It is compatible with OpenAI API specifications, allowing existing applications to integrate seamlessly by simply switching endpoints, while supporting a wide range of open source model families that can run on CPU or GPU, including consumer-grade devices. LocalAI emphasizes privacy and control by ensuring all processing happens locally, keeping data on-device and eliminating external dependencies.
Learn more about LocalAI
Company Information
vLLM
United States
vllm.ai
Videos and Screen Captures
Other Useful Business Software
Ship Agents Faster
Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
Product Details
Platforms Supported
Cloud
Training
Documentation
Support
24/7 Live Support
Online
vLLM Frequently Asked Questions
Q: What kinds of users and organization types does vLLM work with?
vLLM works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does vLLM support in their product?
vLLM supports these languages: English.
Q: What kind of support options does vLLM offer?
vLLM offers support via 24/7 live support and online.
Q: What other applications or services does vLLM integrate with?
vLLM integrates with: DashboardGPT, Database Mart, Docker, DocsGPT, Compute with Hivenet, Hugging Face, KServe, Kubernetes, LFM3, ModelLane, Muse Spark Open, Nemotron 3.5 Bolt, Nemotron 3.5 Super VL, Nemotron 3.5 Transcribe, NGINX, NVIDIA DRIVE, omp, OpenAI, PyTorch, Recursive Flint, and Thunder Compute.
Q: Does vLLM have an API?
Yes, vLLM has an API.
Q: What type of training does vLLM provide?
vLLM provides training in the form of documentation.
