Vllm Overview

由灵感与见解生成，来自 6 来源

介绍

Definition: vLLM stands for Virtual Large Language Model, an open-source library designed for efficient inference and model serving of large language models.
Development: It was originally developed in the Sky Computing Lab at UC Berkeley and leverages a memory allocation algorithm called PagedAttention for better performance.
Performance: Achieves significantly higher throughput compared to other LLM frameworks, offering 24x higher throughput than HuggingFace Transformers and 3.5x higher than HuggingFace Text Generation Inference.
Efficiency: Optimizes memory management using PagedAttention, which reduces memory waste and requires fewer resources to achieve higher output.
Use Cases: Widely used by companies to reduce inference costs and manage increasing traffic with fewer resources.

Algorithm: PagedAttention is inspired by paging techniques in operating systems, facilitating efficient memory management for LLMs.
Key Advantage: Allows non-contiguous storage of attention keys and values, reducing memory waste significantly.
Impact: Leads to 2-4 times throughput improvements over conventional systems by ensuring near-zero waste in KV cache memory.
Use: Emphasizes solving inefficiency problems in Key-Value (KV) cache usage in LLMs.
Performance: Enhances the performance in scenarios involving complex decoding algorithms and long sequences.

Integration: Seamless integration with popular HuggingFace models for ease of use.
Performance: High-throughput serving with various decoding algorithms like parallel sampling and beam search.
Support: Compatible with both NVIDIA and AMD GPUs, including experimental features like multi-LoRA support.
Utility: OpenAI-compatible API server capabilities for online serving.
Innovation: Features like continuous batching and optimized CUDA kernels ensure efficient model execution.

Throughput: Outperforms HuggingFace Transformers with 24x higher throughput and HuggingFace TGI with 3.5x higher throughput.
Efficiency: Requires fewer resources due to its enhanced memory management capabilities.
Popularity: Widely adopted in industry to reduce costs and improve efficiency in managing high-traffic scenarios.
Flexibility: Offers robust performance across diverse hardware configurations and model types.
Ease of Use: Known for its streamlined setup process and user-friendly features compared to competitors like TensorRT-LLM.

Diversity: Supports a wide range of popular open-source models from HuggingFace.
Types: Includes Classic Transformers (Llama, GPT-2, GPT-J), Mixture-of-Expert LLMs, and Multi-modal LLMs.
Configuration: Facilitates integration with various transformer architectures for enhanced capabilities.
Alignment: Adapts to various configuration settings and hardware specifications.
Documentation: Comprehensive support documentation available for ease of access and implementation.

Development: Rapid acceptance and integration within industry and community began in July 2023.
Community: Amassed over 20,000 GitHub stars and includes a thriving community of over 350 contributors.
Industry Use: Supported by major technology companies like AMD, AWS, Databricks, Dropbox, NVIDIA, and Roblox.
Academic Backing: Supported by prestigious institutions like the University of California, Berkeley, and San Diego.
Impact: Widely used to optimize inference in a variety of applications, from chatbots to Software development aids.