In 2026, local deployment of large models has become a necessity for developers, researchers and enterprises. The choice of inference framework can produce 2–6x performance differences on the same hardware. After several recent in-depth technical evaluations, this article synthesizes authoritative test data and real-world deployment experience to comprehensively analyze vLLM, SGLang, llama.cpp, MLX and Ollama, focusing on core principles, advantages and disadvantages, applicable scenarios, and presenting a side-by-side comparison in table form.

1. Overview of Core Principles of the Five Frameworks

1. vLLM — PagedAttention + continuous batching production-grade engine

vLLM is currently the most widely used inference framework in production, both locally and in the cloud. Its main innovation is the PagedAttention mechanism, directly inspired by operating system virtual memory paging.

Traditional Transformers allocate large KV caches during inference and often require all requests in a batch to finish before starting the next batch, causing significant memory waste (utilization commonly only 60–80%) and throughput bottlenecks. vLLM splits the KV cache into fixed-size "pages" (typically 512–2048 tokens) and dynamically allocates and reclaims them much like an OS manages memory. This allows efficient memory reuse even when request sequence lengths vary greatly, boosting memory utilization to over 95%.

At the same time, vLLM introduces Continuous Batching: when one request finishes, a new request is immediately inserted into the execution queue without waiting for the whole batch to complete. This greatly improves GPU utilization, especially in high-concurrency API service scenarios.

vLLM supports multiple backends including NVIDIA, AMD, Google TPU and Intel Gaudi; its ecosystem is mature and integration is straightforward.

Repository: https://github.com/vllm-project/vllm

2. SGLang — agent framework with RadixAttention prefix-tree caching

SGLang, developed by part of the original vLLM team, is a next-generation inference framework designed for complex interactive scenarios. Its core innovation is RadixAttention (a radix-tree attention cache).

While conventional frameworks typically discard the KV cache after a request completes, SGLang persistently stores prefixes of all historical requests in a tree structure (Radix Tree). When a new request arrives, the system intelligently finds the longest matching prefix and reuses the already-computed KV cache, only computing the newly added portion. This is extremely effective in RAG (retrieval-augmented generation), multi-turn dialogue and AI Agent scenarios.

For example, in enterprise knowledge-base Q&A where multiple users query different passages of the same document, SGLang can significantly reduce redundant computation. Official tests show that in high-prefix-hit-rate scenarios SGLang can achieve 3–6.4x speedups versus vLLM. SGLang also includes deep optimizations for structured outputs (JSON modes) and multi-agent collaboration.

Repository: https://github.com/sgl-project/sglang

3. llama.cpp — ultra-light, cross-platform native GGUF engine

llama.cpp, led by Georgi Gerganov, is a classic project often described as the "Swiss Army knife" of local large models. Its core design philosophy is extreme simplicity and portability.

Implemented in pure C/C++ with almost zero external dependencies, it supports a wide range of quantization schemes from 2-bit to FP16. Models are stored in the GGUF format and use mmap (memory mapping) for very fast model loading—even 70B models can load in seconds. It does not depe