AI Tool

Accelerate Your LLM Inference with vLLM Runtime

The Open-Source Solution for Fast, Efficient Serving with Paged Attention

Visit vLLM Runtime
BuildServingvLLM & TGI
vLLM Runtime - AI tool hero image
1Seamless TPU Inference on JAX and PyTorch with No Code Changes
2Maximize Performance with Advanced Memory Management and Batching
3Support for Diverse Model Types and Scalable Backends
4Flexible API Compatibility for Integration with Developer Workflows

Similar Tools

Compare Alternatives

Other tools you might consider

1

vLLM Open Runtime

Shares tags: build, serving, vllm & tgi

Visit
2

OctoAI Inference

Shares tags: build, serving, vllm & tgi

Visit
3

SambaNova Inference Cloud

Shares tags: build, serving, vllm & tgi

Visit
4

Hugging Face Text Generation Inference

Shares tags: build, serving, vllm & tgi

Visit

overview

What is vLLM Runtime?

vLLM Runtime is an open-source inference solution that enhances the performance of large language models (LLMs) with innovative features like paged attention and optimized memory management. Designed for rapid deployment and easy scalability, it accommodates both enterprise-grade applications and research projects.

  • 1Open-source and free to use
  • 2Designed for high-performance LLM serving
  • 3Supports both JAX and PyTorch frameworks

features

Key Features of vLLM Runtime

vLLM Runtime is packed with leading-edge capabilities that enable developers to achieve exceptional performance benchmarks. Experience low-latency inference, enhanced throughput, and reliability for all your LLM tasks.

  • 1Unified runtime for seamless TPU inference
  • 2Production-grade batching and memory optimizations
  • 3Support for multi-modal and encoder-decoder models

use cases

Real-World Applications

Whether you are building interactive generative AI products, deploying reinforcement learning engines, or developing code generation tools, vLLM Runtime is designed to meet your needs. Its flexibility allows for tailored workflows that cater to various use cases.

  • 1Agent frameworks and RL applications
  • 2Long-context support and tool integration
  • 3Compatible with OpenAI APIs for easy migration

Frequently Asked Questions

+What models are supported by vLLM Runtime?

vLLM Runtime supports a variety of models, including recent advancements like Llama, Qwen, and Gemma, ensuring that both JAX and PyTorch can be utilized seamlessly.

+Is vLLM Runtime suitable for enterprise use?

Absolutely! vLLM Runtime is designed for both enterprise-scale applications and research, providing the reliability and scalability necessary for high-impact deployments.

+How do I get started with vLLM Runtime?

Getting started is easy—visit our website at vllm.ai to find documentation, installation guidelines, and examples to kickstart your projects.