S-LoRA: Serving Thousands of Concurrent LoRA Adapters

We introduce S-LoRA (code), a system designed for the scalable serving of many LoRA adapters. S-LoRA adopts the idea of Unified Paging for KV cache and adapter weights to reduce memory fragmentation. Heterogeneous Batching of LoRA computation with different ranks leveraging optimized custom CUDA kernels which are aligned with the memory pool design.

vLLM v.s. DeepSpeed-FastGen

vLLM matches DeepSpeed-FastGen’s speed in common scenarios and surpasses it when handling longer outputs. DeepSpeed-FastGen only outperforms vLLM in scenarios with long prompts and short outputs, due to its Dynamic SplitFuse optimization. This optimization is on vLLM’s roadmap. vLLM’s mission is to build the fastest and easiest-to-use open-source LLM inference and serving engine.

DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Large language models (LLMs) like GPT-4 and LLaMA have emerged as a dominant workload in serving a wide range of applications infused with AI at every level. From general chat models to document summarization, and from autonomous driving to copilots at every layer of the software stack, the demand to deploy and serve these models at scale has skyrocketed.

vLLM: Easy, Fast, and Cheap LLM Serving

LLMs promise to fundamentally change how we use AI across all industries. However, actually serving these models is challenging and can be surprisingly slow even on expensive hardware. We are excited to introduce vLLM, an open-source library for fast LLM inference and serving. vLLM utilizes PagedAttention, our new attention algorithm that effectively manages attention keys and values.

DeepSpeed Chat

ChatGPT-like models have taken the AI world by storm, and it would not be an overstatement to say that its impact on the digital world has been revolutionary. These models are incredibly versatile, capable of performing tasks like summarization, coding, and translation with results that are on-par or even exceeding the capabilities of human experts.