llama.cpp vs vllm

Common questions

What is the difference between llama.cpp and vllm?

llama.cpp: LLM inference in C/C++. vllm: A high-throughput and memory-efficient inference and serving engine for LLMs. See the comparison table for live GitHub stats and shared categories.

When should I choose llama.cpp over vllm?

Choose llama.cpp over vllm when llama.cpp is primarily C++; vllm is Python; License: llama.cpp is MIT, vllm is Apache-2.0; llama.cpp supports various installation methods including package managers (like brew), Docker containers for isolation, pre-built binaries for ease of deployment, and source builds for flexibility; Requirements: Installation can be done via multiple channels including package managers, Docker, and direct downloads.; VLLM and llama.cpp both offer high-throughput and efficient LLM inference engines, thus they are considered alternatives; Tags unique to llama.cpp: c++, ggml; - You need high-performance inference capabilities in a lightweight environment where C++ performance benefits are critical.

When should I choose vllm over llama.cpp?

Choose vllm over llama.cpp when vllm is primarily Python; llama.cpp is C++; License: vllm is Apache-2.0, llama.cpp is MIT; Pricing: vLLM operates under the Apache-2.0 license, so it's entirely free to use without direct monetary costs, but users might incur costs related to hardware and cloud services required for deployment.; Requirements: Installation can be done via uv pip install vllm or by building from source, allowing flexibility in how the tool is set up.; VLLM and llama.cpp both offer high-throughput and efficient LLM inference engines, thus they are considered alternatives; Tags unique to vllm: amd, cuda, deepseek, gpt; When you need to deploy large language models with requirements for both high throughput and low resource consumption.

When should I avoid llama.cpp?

- If you prefer a language other than C++, as this tool lacks support for Python or JavaScript bindings that provide higher-level abstractions. - When your project demands extensive runtime customization and flexibility that is more easily achieved in languages like Python with libraries such as PyTorch.

When should I avoid vllm?

Avoid using vLLM if your application strictly limits itself to a single type of hardware without needing cross-platform compatibility, as it may introduce unnecessary complexity. If memory efficiency is not a concern and you are optimizing for simplicity over resource management, alternatives with less configuration might be preferable.

Is llama.cpp or vllm more popular on GitHub?

llama.cpp has more GitHub stars (122,941 vs 87,847). Stars measure visibility, not whether either tool fits your constraints.

Are llama.cpp and vllm open source?

Yes - both are open-source projects on GitHub (llama.cpp: MIT, vllm: Apache-2.0).

Where can I find alternatives to llama.cpp or vllm?

GraphCanon lists graph-backed alternatives at llama.cpp alternatives and vllm alternatives (llama.cpp markdown twin, vllm markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, llama.cpp or vllm?

llama.cpp: Very active. vllm: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for llama.cpp and vllm?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama.cpp trust report; vllm trust report.