Comprehensive Analysis and Selection Guide for Large Language Model (LLM) Inference Frameworks (2025 Edition)

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.
• LMDeploy: Synonymous with peak GPU performance, delivering ultra-low latency and high throughput for enterprise-grade real-time applications.
• TGI (Text Generation Inference): A production-ready text generation service optimized for stability and high throughput, serving as the backbone for reliable LLM services.
• SGLang: A high-performance runtime for language generation, featuring deep optimizations and built-in distributed deployment capabilities for complex scenarios.
• DeepSeek AI Open Infra Index (Underlying Optimization Support): DeepSeek AI’s open-source infrastructure tools (e.g., FlashMLA, DeepEP) enhance frameworks like SGLang and vLLM, significantly boosting inference efficiency at the hardware level.

Local Deployment & Lightweight Frameworks

• Ollama: A minimalist local deployment tool with one-click model loading and a user-friendly web interface, perfect for rapid prototyping.
• Llama.cpp: A CPU-optimized, lightweight solution for edge devices and resource-constrained environments.
• LocalAI: Prioritizes data privacy and security, ideal for sensitive on-premises applications.
• KTransformers: A CPU-focused framework balancing energy efficiency and performance in low-resource settings.
• GPT4ALL: Features a GUI for effortless LLM experimentation, lowering the barrier to entry for beginners.

Flexible Deployment & Multi-Model Support

• XInference: An open-source framework with OpenAI-compatible APIs, supporting diverse models for agile deployment.
• OpenLLM: A highly customizable open-source solution for mixed-model architectures and hybrid deployments.
• Hugging Face Transformers: Boasts the richest model ecosystem and community support, widely used in research and prototyping.
• LiteLLM: A lightweight API adapter unifying access to multiple LLMs, simplifying multi-model integration.

Developer-Friendly Frameworks

• FastAPI: A high-performance Python web framework for rapid LLM API development.
• Dify: A low-code platform for building and deploying LLM applications.
• ChatTool: Tailored for chatbot and customer service applications, offering dialogue management and model invocation features.

3. In-Depth Framework Analysis & Comparison

We dissect five core frameworks—XInference, LiteLLM, LMDeploy, SGLang, and vLLM—and present a comparative table (Section 3.7) across key dimensions: performance, usability, flexibility, and community support.

3.1 XInference: Flexible Model Serving Platform

• Key Features: OpenAI-compatible APIs, multi-model support, and cloud/on-premises adaptability.
• Advantages: Full lifecycle model management, high usability, and seamless integration.
• Use Cases: Startups and research teams requiring rapid iteration and flexible deployment.

3.2 LiteLLM: Lightweight Multi-Model API Integrator

• Key Features: Unified OpenAI-style API for multiple providers (OpenAI, Anthropic, Hugging Face, DeepSeek).
• Advantages: Built-in caching, rate limiting, and plug-and-play model switching.
• Use Cases: Multi-model testing, high-availability production environments.

3.3 LMDeploy: GPU Performance Maximizer

• Key Features: Optimized for LLMs and vision-language models (VLs), squeezing GPU potential for high throughput.
• Advantages: Enterprise-grade stability, broad model compatibility, and low-latency inference.
• Use Cases: Real-time dialogue systems, large-scale content generation platforms.

3.4 SGLang: High-Performance Distributed Inference Pioneer

• Key Features: Python-based runtime with dynamic batching, distributed deployment, and backend flexibility (vLLM, DeepSeek-Kit).
• Latest Updates (Feb 2025): Supports FP8 inference for DeepSeek-R1, achieving 1000+ tokens/sec in benchmarks.
• Use Cases: Prototyping, long-text/code generation, and cloud-scale distributed inference.

3.5 vLLM: Leader in GPU-Optimized Inference

• Key Features: PagedAttention for memory efficiency, dynamic batching, and streaming output.
• Advantages: Industry-leading throughput, optimized for large models and high concurrency.
• Use Cases: Enterprise-scale LLM deployment, AI chatbots, and high-traffic services.

3.6 DeepSeek AI Open Infra Index: Foundation for LLM Optimization

• Components:
• FlashMLA: High-efficiency MLA decoding kernel for Hopper GPUs (e.g., H800), nearing 3000 GB/s bandwidth.
• DeepEP: Expert Parallelism (EP) library for MoE models, supporting FP8 and RDMA/NVLink.
• DeepGEMM: FP8 GEMM library optimized for Hopper GPUs, achieving 1350+ TFLOPS.
• Optimized Parallel Strategies: Techniques like DualPipe and EPLB to accelerate training for models like DeepSeek-V3/R1.
• Use Cases: Custom high-performance kernels, distributed MoE deployment, latency-sensitive applications (e.g., finance, gaming AI).

3.7 Framework Comparison Table

(Full table extended for other frameworks in original text.)

5. Scenario-Based Selection Recommendations

• Resource-constrained local environments: Ollama or Llama.cpp (lightweight, CPU-optimized).
• GPU-optimized performance: LMDeploy or vLLM (high throughput, low latency).
• Rapid API development: LiteLLM (unified API) or FastAPI (quick prototyping).
• Flexible model management: XInference or OpenLLM (multi-model, cloud-native).
• Enterprise-scale deployment: vLLM/TGI (stability, scalability).
• Distributed high-throughput systems: SGLang (with Kubernetes/SkyPilot).
• Low-level optimization: DeepSeek Open Infra Index (FP8, MoE support).

6. Conclusion & Future Outlook

This guide highlights the 2025 LLM inference landscape, where SGLang excels in distributed performance, vLLM/LMDeploy dominate GPU efficiency, and DeepSeek AI’s Open Infra Index unlocks new optimization frontiers. As frameworks evolve, trends like FP8 adoption and MoE-specific tooling will shape the next generation of LLM deployment.

7. Key References