Abstract
This guide, updated as of February 27, 2025, provides an in-depth analysis of mainstream LLM inference frameworks (e.g., XInference, LiteLLM, LMDeploy, SGLang, vLLM) in terms of functionality, performance, usability, and application scenarios. Incorporating insights from DeepSeek AI’s Open Infrastructure Index (including FlashMLA, DeepEP, DeepGEMM, and optimized parallel strategies), this paper emphasizes underlying technical principles, community ecosystems, and future trends. It serves as a strategic reference for AI developers, researchers, and enterprise decision-makers, facilitating optimal LLM inference framework selection in the 2025 technological landscape.
1. Introduction
As of February 27, 2025, large language models (LLMs) have become pivotal in transforming fields such as intelligent customer service, content generation, and code automation. Inference frameworks, as critical components for efficient LLM deployment, directly impact application performance, cost, and development efficiency. To help readers navigate the diverse framework landscape, this article systematically evaluates current mainstream LLM inference frameworks, integrating insights from DeepSeek AI’s Open Infrastructure Index (Open Infra Index). By focusing on foundational technologies, ecosystem maturity, and future directions, we aim to provide actionable guidance for strategic decision-making.
2. Overview of Mainstream LLM Inference Frameworks
Below is a categorized overview of 2025’s leading LLM inference frameworks, highlighting their core strengths and the role of DeepSeek AI’s Open Infra Index in performance enhancement:
High-Performance Inference Frameworks
- vLLM: A GPU-optimized framework leveraging PagedAttention for exceptional throughput and memory efficiency, ideal for large-scale, high-concurrency deployments.