llama.cpp vs SGLang: Head-to-Head Comparison

Quick Verdict

llama.cpp is the better pick for efficient CPU and edge inference. SGLang is the better pick for high-performance serving with structured generation.

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At a Glance

Feature	llama.cpp	SGLang
Best For	Efficient CPU and edge inference	High-performance serving with structured generation
Pricing	Free and open source	Free and open source
Free to Start	Yes	Yes
License	Open source	Open source
Deployment	Runs locally	Self-hosted
Link	Visit llama.cpp	Visit SGLang

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Detailed Breakdown

llama.cpp

LLM inference in C/C++

Pros:

• Runs on CPUs and Apple Silicon
• GGUF quantization
• Minimal dependencies

Cons:

• Lower-level tooling
• Manual configuration

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SGLang

Fast serving framework for LLMs and vision models

Pros:

• RadixAttention prefix caching
• Fast structured outputs
• Strong multi-GPU support

Cons:

• Requires GPU expertise
• Younger than vLLM

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Key Differences

• Positioning: llama.cpp — LLM inference in C/C++. SGLang — fast serving framework for LLMs and vision models.
• Both share the same licensing model (open source), so the decision comes down to features and workflow fit.
• Deployment: llama.cpp — runs locally. SGLang — self-hosted.
• Pricing: llama.cpp — free and open source. SGLang — free and open source.
• Signature strength: llama.cpp — runs on CPUs and Apple Silicon. SGLang — RadixAttention prefix caching.

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Frequently Asked Questions

Is llama.cpp better than SGLang?

It depends on your requirements. llama.cpp is a strong fit for efficient CPU and edge inference, while SGLang suits high-performance serving with structured generation.

Is llama.cpp free to use?

Yes, you can start with llama.cpp for free. Pricing model: Free and open source.

Is SGLang free to use?

Yes, you can start with SGLang for free. Pricing model: Free and open source.

Can I self-host llama.cpp or SGLang?

llama.cpp runs locally on your own machine. SGLang can be self-hosted. Deployment options: self-hosted.

What are the main drawbacks of llama.cpp and SGLang?

llama.cpp: lower-level tooling; manual configuration. SGLang: requires GPU expertise; younger than vLLM.