vLLM

Overview

vLLM is an open-source large model inference and serving engine. Its core uses PagedAttention technology to manage the attention key-value cache, reducing GPU memory fragmentation through a paging mechanism, and significantly improving throughput with continuous batching. It supports tensor parallelism, pipeline parallelism, speculative decoding, multiple quantization schemes, and LoRA adaptation, provides an OpenAI-compatible HTTP server, and can be deployed on various hardware such as NVIDIA GPUs, AMD GPUs, and TPUs. With its high throughput and ease of integration, vLLM is used as an underlying engine by many cloud providers and inference platforms. On September 24, 2026, vLLM announced support for lossless text watermarking based on the Gumbel-max algorithm, integrating watermarking capability into the sampling pipeline of Model Runner v2, maintaining output diversity while remaining compatible with speculative decoding, and providing traceable identification capability for generated content.

Key Features

• PagedAttention Key-Value Cache Management: Manages the attention key-value cache in pages, reducing GPU memory fragmentation and significantly improving inference throughput together with continuous batching.
• OpenAI-Compatible Server: Provides an OpenAI-compatible HTTP server, making it easy for existing applications to migrate and integrate seamlessly.
• Multi-Hardware and Parallel Support: Supports tensor parallelism, pipeline parallelism, speculative decoding, multiple quantization schemes, and LoRA adaptation, and can be deployed on various hardware such as NVIDIA GPUs, AMD GPUs, and TPUs.
• Lossless Text Watermarking: On September 24, 2026, lossless watermarking based on the Gumbel-max algorithm was added, integrated into the Model Runner v2 sampling pipeline. It is implemented through three changes: fused GPU kernel, dual-key scheme, and context deduplication (PR 54053, 56122, 56233), is compatible with speculative decoding, and maintains output diversity.

Use Cases

• Cloud providers and inference platforms use it as an underlying inference engine to support high-concurrency large model services.
• Enterprises deploy OpenAI-compatible private large model API services.
• Batch text generation scenarios that require high throughput and low GPU memory fragmentation.
• Compliance and content review scenarios that require traceable identification of generated content.
• Model inference deployment in multi-hardware environments, including NVIDIA GPUs, AMD GPUs, and TPUs.

Pros

• Open-source engine with an active community, adopted by many cloud providers and inference platforms.
• The combination of PagedAttention and continuous batching significantly improves throughput and reduces GPU memory fragmentation.
• Provides an OpenAI-compatible server with low integration cost.
• Supports tensor parallelism, pipeline parallelism, speculative decoding, multiple quantization schemes, and LoRA adaptation, offering strong extensibility.
• Can be deployed on various hardware such as NVIDIA GPUs, AMD GPUs, and TPUs, providing flexible hardware choices.
• Adds lossless text watermarking, compatible with speculative decoding while maintaining output diversity, providing traceable identification capability.

Pricing

vLLM is an open-source project. For specific pricing and commercial support information, please refer to the official website.

Summary

vLLM is an open-source large model inference and serving engine that achieves high throughput with PagedAttention and continuous batching, provides an OpenAI-compatible server, supports multiple parallelism, quantization, and LoRA adaptation options, and can be deployed on various hardware. In September 2026, lossless text watermarking was added, compatible with speculative decoding while maintaining output diversity, providing traceable identification for generated content.

Version History

• vLLM adds distortion-free text watermarking based on Gumbel-max (2026-09-24): Watermarking is integrated into the Model Runner v2 sampling pipeline through fused GPU kernels, a dual-key scheme and context deduplication, staying compatible with speculative decoding while preserving output diversity and giving generated content a traceable marker.
• vLLM releases vllm-metal, supporting concurrent serving on Apple Silicon, providing more balanced performance under concurrent agent workloads. (2026-09-22): vLLM releases vllm-metal, supporting concurrent serving on Apple Silicon, providing smoother TTFT under concurrent agent workloads.