vLLM

Basic Information

• Company/Brand: vLLM Project (Originated from UC Berkeley)
• Country/Region: USA
• Official Website: https://vllm.ai
• Type: High-performance LLM inference engine
• Founded: 2023 (Started as an academic project)

Product Description

vLLM is a high-throughput, memory-efficient inference and serving engine for large language models, originating from academic research at UC Berkeley. Its core innovation, the PagedAttention algorithm, draws inspiration from operating system virtual memory management, achieving near-zero memory waste in KV cache management. The v0.7.3 version in 2026 is optimized for NVIDIA Blackwell (B200) GPUs and continues to add cutting-edge features like elastic expert parallelism.

Core Features/Characteristics

• PagedAttention: Revolutionary attention algorithm with near-zero memory waste in KV caching
• Continuous Batching: New requests are immediately added to the batch without waiting for a fixed batching window
• Speculative Decoding: N-gram speculative decoding on GPUs, compatible with asynchronous schedulers
• KV Cache Offloading: Intelligent CPU offloading with the new FlexKV backend
• Elastic Expert Parallelism: Dynamic GPU scaling for MoE models (Milestone 2)
• GPU-Independent Preprocessing: New vllm launch render command separates multimodal preprocessing
• Multi-Platform Support: NVIDIA, AMD ROCm, Intel XPU, TPU
• Blackwell Optimization: Specialized optimizations for NVIDIA B200 GPUs
• H100 Performance: Approximately 12,500 tokens/s (slightly behind SGLang and LMDeploy's 16,200 tokens/s)

Business Model

• Fully Open Source: Apache 2.0 license
• Community-Driven: GitHub open-source project with 73,000+ stars
• Enterprise Support: Some core team members have established a commercial company to provide enterprise support
• Cloud Platform Integration: Integrated services provided by NVIDIA, AWS, etc.

Target Users

• Enterprises and teams requiring high-throughput LLM services
• AI infrastructure engineers
• Model service platform operators
• Organizations needing GPU cluster inference optimization
• Cloud service providers

Competitive Advantages

• Industry-leading memory efficiency with PagedAttention algorithm
• High ecosystem maturity with seamless integration into mainstream frameworks and platforms
• Extensive multi-GPU and multi-platform support
• Continuous batching and speculative decoding reduce latency
• Elastic expert parallelism for efficient inference of MoE models
• Active open-source community and rapid iteration

Market Performance

• 73,000+ stars on GitHub, making it the most popular LLM inference engine
• Widely adopted by numerous AI companies and platforms as an inference backend
• Slightly behind SGLang/LMDeploy in throughput but boasts the highest ecosystem maturity
• Official integration and optimization support from NVIDIA
• Significant academic influence with the PagedAttention paper widely cited

Relationship with the OpenClaw Ecosystem

vLLM primarily targets high-performance, high-throughput server-side deployment scenarios. For OpenClaw users building private LLM inference services, vLLM offers an efficient inference backend option. Users can deploy vLLM for local or private cloud model inference services and connect them to the OpenClaw platform via its OpenAI-compatible API, making it particularly suitable for enterprise-level deployments requiring simultaneous service for multiple agents.