VLLM
vLLM is an open-source, high-throughput LLM inference and serving engine (PyTorch Foundation project) serving 200+ model architectures across 10+ hardware platforms, used by AI developers, researchers, and enterprises globally under Apache 2.0.
- Company typePrivate
- Founded2023
- HeadquartersSan Francisco, United States
- Headcount—
- GTM typeB2B
- OfferingSoftware
What VLLM does
vLLM is an open-source high-throughput LLM inference and serving engine originally developed in 2023 at UC Berkeley's Sky Computing Lab and subsequently transitioned to the PyTorch Foundation for governance in 2024. Its core technology centers on PagedAttention, a memory-management innovation that reduces KV-cache fragmentation, combined with continuous batching, speculative decoding, CUDA/HIP graph execution, and broad quantization support (FP8, INT8, INT4, GPTQ/AWQ, GGUF, etc.). The platform serves 200+ model architectures — including decoder-only LLMs, mixture-of-experts, hybrid attention/SSM, multimodal, and embedding models — across 10+ hardware platforms spanning NVIDIA, AMD, Google TPU, Intel Gaudi/XPU, Huawei Ascend, AWS Neuron, Apple Silicon, and emerging accelerators. It exposes an OpenAI-compatible REST API (plus Anthropic Messages API and gRPC) and integrates with the broader PyTorch, HuggingFace, and Kubernetes ecosystems via companion tools including LLM Compressor, Speculators, Semantic Router, Production Stack, and AIBrix.
The project's go-to-market is community-led and PLG-style: source code is distributed freely under Apache 2.0 via GitHub, PyPI, and Docker Hub, with developer acquisition through documentation, recipes, benchmarks, Slack, and forums. vLLM generates no traditional revenue; sustainability is funded through cash donations (a16z, Sequoia Capital, Skywork AI, ZhenFund via GitHub Sponsors and OpenCollective) and compute donations from NVIDIA, AMD, AWS, Google Cloud, Intel, Alibaba Cloud, IBM, Red Hat, Anyscale, and several smaller GPU cloud providers. Its customers are horizontally distributed across AI/ML developers and researchers building LLM applications, enterprises deploying production LLM serving, and cloud providers offering GPU infrastructure.
VLLM firmographics
Firmographics- Name
- VLLM
- Legal name
- vLLM
- Website
- https://vllm.ai
- Company type
- Private
- Founded year
- 2023
- Operating status
- Operating
- Short description
- vLLM is an open-source, high-throughput LLM inference and serving engine (PyTorch Foundation project) serving 200+ model architectures across 10+ hardware platforms, used by AI developers, researchers, and enterprises globally under Apache 2.0.
- Ownership category
- akta.pro rank
VLLM industry classification
Industry- Product category
- LLM Inference Software
- NAICS
- Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (518)
- SIC
- Services-Prepackaged Software (7372)
- akta.pro primary industry
- Hybrid Cloud Compute & Virtualization Stack (HCI/VMs/Kubernetes) (HDABAMAF)
Keywords
Where VLLM is headquartered
LocationHeadquarters
- HQ city
- San Francisco
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
VLLM business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Technology or R&D, Personnel, Infrastructure, Operations, Marketing or Sales
Revenue model
- Open Source Distribution: vLLM is an open-source project distributed freely under Apache 2.0 license. The project accepts donations through GitHub Sponsors and OpenCollective to support development, maintenance, and adoption.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | — | Open Source (Free) |
Go-to-market motion1 record
Distribution channels4 records
Marketing channels8 records
VLLM product offering
Product offeringCore offering
vLLM is an open-source, high-throughput LLM inference and serving engine that runs large language models efficiently on diverse hardware via its PagedAttention memory management technique. It exposes an OpenAI-compatible HTTP server with Chat, Embeddings, and Completion endpoints, supports continuous batching, quantization, speculative decoding, and distributed inference, and is distributed for free under the Apache 2.0 license via GitHub, PyPI, Docker, and Hugging Face.
Product overview
vLLM is a unified open-source LLM inference and serving platform built around the vLLM Core Engine, which provides high-throughput, memory-efficient inference via PagedAttention and continuous batching. The platform includes ecosystem tools for the complete ML lifecycle: vLLM Recipes for deployment guides, vLLM Performance Dashboard for benchmarking, and vLLM Roadmap for project planning. The ecosystem extends with specialized modules including LLM Compressor for model quantization, Speculators for speculative decoding acceleration, Semantic Router for intelligent request routing, Production Stack for enterprise deployment, AIBrix for Kubernetes orchestration, GuideLLM for performance evaluation, and vLLM Omni for multi-modal (vision, audio, video) inference support. The platform supports 200+ model architectures across decoder-only LLMs, mixture-of-expert models, hybrid attention/SSM models, and multi-modal models on NVIDIA, AMD, Intel, and other hardware.
Differentiator
Problem solved
Functional benefit
Brands
- vLLM Recipes: Community-maintained recipes for deploying models on various hardware configurations including NVIDIA H100/H200/B200/B300, Grace-Blackwell, and AMD MI300X/MI325X/MI355X.
- vLLM Omni
- vLLM Playground
- LLM Compressor
- Production Stack
- Semantic Router
- Speculators
Products and services
- vLLM Core Engine The open-source high-throughput LLM inference and serving engine with PagedAttention memory management, continuous batching, CUDA/HIP graph integration, quantization, speculative decoding, multi-LoRA, distributed inference, and an OpenAI-compatible HTTP server for Chat, Embeddings, and Completion endpoints across 200+ model architectures.
Quantifiable outcome
- 23x throughput improvement with continuous batching
- +1 more outcomes
Companies that use VLLM
Customer profileSegments2 records
Ideal customer profiles2 records
VLLM technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration35 records
AI capability14 records
Feature10 records
VLLM partnerships and signals
Strategic signalPartnerships
Twelve partnerships are on record, tiered core and flagship.
- NVIDIAcoreNVIDIA provides compute resources and is a primary hardware platform supported by vLLM with optimized CUDA kernels, TRTLLM integration, and Blackwell/Grace-Hooper support.
- AMDcoreAMD provides compute resources and ROCm support for running vLLM on AMD GPUs including MI300X/MI325X/MI355X.
- Google CloudcoreGoogle Cloud provides compute resources and vLLM supports Google Cloud TPUs and TPU inference.
- AWScoreAWS provides compute resources and vLLM supports AWS Neuron Accelerator for inference on AWS infrastructure.
- IntelcoreIntel provides compute resources and vLLM supports Intel Gaudi accelerators and XPU GPUs.
- Alibaba CloudcoreAlibaba Cloud provides compute resources for vLLM development and testing.
- UC BerkeleyflagshipvLLM was originally developed in the Sky Computing Lab at UC Berkeley and continues to have strong academic ties with ongoing research collaboration.
- AnyscalecoreAnyscale provides compute resources and vLLM has integration with Ray for distributed serving.
- Red HatcoreRed Hat provides compute resources and vLLM supports enterprise deployment via Red Hat ecosystem.
- RunPodcoreRunPod provides compute resources and vLLM has deployment integration with RunPod for cloud GPU instances.
- PyTorch FoundationflagshipvLLM is a PyTorch Foundation project, indicating strong institutional backing and integration with the PyTorch ecosystem.
- Hugging FacecoreSeamless integration with Hugging Face models and ecosystem for model loading and deployment.
Scale indicators5 records
Recent moves6 records
Expansion highlights6 records
VLLM competitors and assessment
Company assessmentDirect peers
- NVIDIA TensorRT-LLM: NVIDIA's optimized LLM inference engine that directly competes with vLLM for high-throughput serving on NVIDIA hardware. Both target state-of-the-art throughput on open and proprietary LLMs, but TensorRT-LLM benefits from NVIDIA's tight hardware-software integration.
- llama.cpp: A lightweight, widely-used C++ inference engine for running LLMs locally on consumer hardware. llama.cpp overlaps with vLLM's CPU/edge support and is a partial competitor for on-device and quantized serving scenarios.
- Hugging Face Text Generation Inference (TGI): Hugging Face's Rust-based LLM serving engine. TGI is the most direct open-source alternative to vLLM for serving HuggingFace models, competing on throughput, latency, and ease of integration with the broader HuggingFace ecosystem.
- SGLang: An emerging open-source LLM serving framework co-developed at UC Berkeley Sky Computing Lab. SGLang targets similar workloads as vLLM with a focus on structured generation and agentic workflows, competing for the same developer mindshare.
Broad incumbents
- Anyscale (Ray Serve): Anyscale provides Ray-based distributed serving infrastructure and is a compute partner and deployment integration for vLLM. It is partially competitive for production-scale LLM serving and offers Ray-native alternatives to vLLM's distributed inference features.
- NVIDIA Triton Inference Server: NVIDIA's broader inference server supporting multiple model types and backends. Triton overlaps with vLLM in LLM serving but covers a wider scope (vision, speech, custom models) and benefits from NVIDIA's enterprise-grade support and tooling.
Emerging players
- Modal: A serverless cloud platform with first-class GPU and LLM inference support. Modal is listed as a vLLM deployment integration and represents an emerging adjacent competitor for hosted LLM serving, especially for AI-native startups.
- Ollama: A local LLM serving platform built on llama.cpp that simplifies model deployment for developers and consumers. Ollama overlaps with vLLM at the developer/edge tier but focuses on single-machine ease-of-use rather than multi-GPU production serving.
- LMDeploy: An LLM serving framework from InternLM/Shanghai AI Lab focused on high-throughput inference. LMDeploy is a partial competitor offering similar tensor-parallel and quantization capabilities, primarily used in the Chinese open-source LLM ecosystem.
- DeepSpeed-MII: Microsoft's open-source LLM inference library built on DeepSpeed. DeepSpeed-MII targets similar low-latency, high-throughput serving workloads as vLLM, with tight integration into the Azure ML and ONNX Runtime ecosystems.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat6 records
Key risks7 records
Key highlights7 records
Customer concentration
VLLM social profiles
Digital presenceVLLM financial estimates
Financial estimateRevenue estimate
Valuation estimate
VLLM leadership team
Management profileNumber of profiles
VLLM funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
VLLM M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about VLLM
What does VLLM do?
vLLM is an open-source, high-throughput LLM inference and serving engine that runs large language models efficiently on diverse hardware via its PagedAttention memory management technique. It exposes an OpenAI-compatible HTTP server with Chat, Embeddings, and Completion endpoints, supports continuous batching, quantization, speculative decoding, and distributed inference, and is distributed for free under the Apache 2.0 license via GitHub, PyPI, Docker, and Hugging Face.
Is VLLM a public or private company?
VLLM is a private company. It is classified as nonprofit foundation owned and is currently operating.
When was VLLM founded?
VLLM was founded in 2023.
Where is VLLM based?
VLLM is headquartered in San Francisco, United States, in the North America region.
How does VLLM make money?
One revenue line is on record: open Source Distribution.
Who are VLLM's main competitors?
Direct peers on record are NVIDIA TensorRT-LLM, llama.cpp, Hugging Face Text Generation Inference (TGI) and SGLang. Broad incumbents are Anyscale (Ray Serve) and NVIDIA Triton Inference Server. Emerging players are Modal, Ollama, LMDeploy and DeepSpeed-MII.
Does VLLM have an API?
Yes. vLLM provides an OpenAI-compatible API server, plus Anthropic Messages API and gRPC support. The API enables developers to serve open-source LLMs with drop-in OpenAI compatibility for instant integration. The documentation also shows MCP (Model Context Protocol) tool endpoints. Developer documentation is at docs.vllm.ai.
What industry is VLLM in?
VLLM's product category is LLM Inference Software. Its primary akta.pro industry code is HDABAMAF, Hybrid Cloud Compute & Virtualization Stack (HCI/VMs/Kubernetes). Its NAICS code is 518 and its SIC code is 7372.