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vLLM Runtime
Shares tags: build, serving, vllm & tgi
AI Tool
vLLM Open Runtime
Harness the power of high-throughput, memory-efficient inference with vLLM.
Visit vLLM Open Runtime→
BuildServingvLLM & TGI
1Achieve 1.7x speed improvements with our advanced V1 architecture.
2Deploy across a variety of hardware for ultimate flexibility.
3Experience production-ready features that streamline your workflow.
overview
What is vLLM?
vLLM Open Runtime is an open-source inference stack that provides unparalleled throughput and memory efficiency for serving large language models. With its innovative paged KV cache, it ensures optimal performance, making it the go-to solution for developers worldwide.
- 1Open-source and community-driven.
- 2Specifically designed for high-performance LLM serving.
- 3Flexibly integrates with existing ecosystems.
features
Key Features
vLLM is packed with cutting-edge features that cater to diverse deployment scenarios. From automatic prefix caching to support for various hardware, it equips users with everything needed for seamless LLM serving.
- 1Automatic prefix caching reduces latency significantly.
- 2Chunked prefill ensures stable inter-token latency.
- 3Speculative decoding speeds up token generation.
use cases
Ideal Use Cases
Designed for a variety of applications, vLLM is perfect for companies seeking to leverage large language models in production. Its enterprise-ready capabilities make it suitable for both startups and large organizations alike.
- 1Real-time conversational AI systems.
- 2Automated content generation.
- 3Dynamic text analysis and processing.
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Frequently Asked Questions
+What is the pricing model for vLLM?
vLLM operates on a paid pricing model, offering various tiers to cater to different organizational needs.
+How does vLLM support multiple hardware?
vLLM supports a wide range of hardware including NVIDIA GPUs, AMD devices, Intel CPUs, TPUs, and more, ensuring optimal performance across different environments.
+What are the production-ready features of vLLM?
vLLM includes features such as automatic prefix caching, advanced speculative decoding, and structured output generation, all designed to deliver low-latency and high-throughput inference.