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Comparison

llama.cpp vs airllm

llama.cpp (LLM inference in C/C++) vs airllm (AirLLM for large language model inference on lightweight GPUs) - live GitHub stats and typed graph relationships, not marketing.

Markdown twin · llama.cpp alternatives · airllm alternatives

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llama.cpp

ggml-org/llama.cpp

★ 120kpushed Jul 8, 2026

airllm

lyogavin/airllm

★ 22kpushed Jul 8, 2026

Tagline

llama.cpp: LLM inference in C/C++
airllm: AirLLM for large language model inference on lightweight GPUs

Stars

llama.cpp: 120k
airllm: 22k

Forks

llama.cpp: 20k
airllm: 2.6k

Open issues

llama.cpp: 1.8k
airllm: 106

Language

llama.cpp: C++
airllm: Jupyter Notebook

Adopt for

llama.cpp: A C/C++ library for performing large language model (LLM) inference with minimal setup, enabling state-of-the-art performance across various hardware architectures.
airllm: AirLLM is a tool designed to dramatically reduce inference memory usage for large language models (LLMs), enabling them to run on lightweight GPUs. It supports running large models like AirLLM 70B, Llama3.1 (405B), and Q

Persona

llama.cpp: -
airllm: -

Runtime

llama.cpp: -
airllm: -

License

llama.cpp: MIT
airllm: Apache-2.0

Last pushed

llama.cpp: Jul 8, 2026
airllm: Jul 8, 2026

	llama.cpp	airllm
Tagline	LLM inference in C/C++	AirLLM for large language model inference on lightweight GPUs
Stars	120k	22k
Forks	20k	2.6k
Open issues	1.8k	106
Language	C++	Jupyter Notebook
Adopt for	A C/C++ library for performing large language model (LLM) inference with minimal setup, enabling state-of-the-art performance across various hardware architectures.	AirLLM is a tool designed to dramatically reduce inference memory usage for large language models (LLMs), enabling them to run on lightweight GPUs. It supports running large models like AirLLM 70B, Llama3.1 (405B), and Q
Persona	-	-
Runtime	-	-
License	MIT	Apache-2.0
Last pushed	Jul 8, 2026	Jul 8, 2026
Categories	Inference & Serving	Inference & Serving
Trust and health
Open issues (now)	1.8k	106
Owner type	Organization	User
Security scan	No criticals	4 low (4 low)
Full report	Trust report	Trust report
Typed relationship	llama.cpp alternative airllmBoth airllm and llama.cpp offer lightweight GPU inference options for large language models, differing mainly in their implementation and optimization approaches.

Choose llama.cpp if…

llama.cpp is primarily C++; airllm is Jupyter Notebook.
License: llama.cpp is MIT, airllm is Apache-2.0.
Requirements: - No external dependencies required for C/C++ implementation.; - Custom CUDA kernels support running LLM on NVIDIA GPUs..
Both airllm and llama.cpp offer lightweight GPU inference options for large language models, differing mainly in their implementation and optimization approaches.
Tags unique to llama.cpp: rest-api, hugging-face, c++, llm-inference.
When you require a lightweight and dependency-free solution for LLM inference that supports multiple hardware architectures including x86, ARM, and RISC-V.

When NOT to use llama.cpp

If you are working in an ecosystem requiring heavy use of high-level languages such as Python or Java, given `llama.cpp`'s focus on C/C++ and low-level optimizations.
When developing applications that need frequent API changes, as the updates in `libllama` and `llama-server` REST API might not align with your application’s release cycle.

GitHub Details

Choose airllm if…

airllm is primarily Jupyter Notebook; llama.cpp is C++.
License: airllm is Apache-2.0, llama.cpp is MIT.
Both airllm and llama.cpp offer lightweight GPU inference options for large language models, differing mainly in their implementation and optimization approaches.
Tags unique to airllm: llama, chinese-llm, llm, instruct-gpt.
You should use AirLLM if you need to run very large models such as Qwen3-235B or DeepSeek-V3 (671B) on lower-end GPUs like a single 3GB, 8GB, or ~12GB card without resorting to quantization, distill

When NOT to use airllm

Avoid using AirLLM if you require running models that are not supported by the tool or if your inference environment does not align with its lightweight GPU requirements. If your infrastructure can n

GitHub Details

Explore

llama.cpp trust report →airllm trust report →Inference & Serving category →All comparisons Stack workflows Trending tools

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Common questions

What is the difference between llama.cpp and airllm?: llama.cpp: LLM inference in C/C++. airllm: AirLLM for large language model inference on lightweight GPUs. See the comparison table for live GitHub stats and shared categories.
When should I choose llama.cpp over airllm?: Choose llama.cpp over airllm when llama.cpp is primarily C++; airllm is Jupyter Notebook; License: llama.cpp is MIT, airllm is Apache-2.0; Requirements: - No external dependencies required for C/C++ implementation.; - Custom CUDA kernels support running LLM on NVIDIA GPUs.; Both airllm and llama.cpp offer lightweight GPU inference options for large language models, differing mainly in their implementation and optimization approaches; Tags unique to llama.cpp: rest-api, hugging-face, c++, llm-inference; When you require a lightweight and dependency-free solution for LLM inference that supports multiple hardware architectures including x86, ARM, and RISC-V.
When should I choose airllm over llama.cpp?: Choose airllm over llama.cpp when airllm is primarily Jupyter Notebook; llama.cpp is C++; License: airllm is Apache-2.0, llama.cpp is MIT; Both airllm and llama.cpp offer lightweight GPU inference options for large language models, differing mainly in their implementation and optimization approaches; Tags unique to airllm: llama, chinese-llm, llm, instruct-gpt; You should use AirLLM if you need to run very large models such as Qwen3-235B or DeepSeek-V3 (671B) on lower-end GPUs like a single 3GB, 8GB, or ~12GB card without resorting to quantization, distill.
When should I avoid llama.cpp?: If you are working in an ecosystem requiring heavy use of high-level languages such as Python or Java, given `llama.cpp`'s focus on C/C++ and low-level optimizations. When developing applications that need frequent API changes, as the updates in `libllama` and `llama-server` REST API might not align with your application’s release cycle.
When should I avoid airllm?: Avoid using AirLLM if you require running models that are not supported by the tool or if your inference environment does not align with its lightweight GPU requirements. If your infrastructure can n
Is llama.cpp or airllm more popular on GitHub?: llama.cpp has more GitHub stars (119,640 vs 22,274). Stars measure visibility, not whether either tool fits your constraints.
Are llama.cpp and airllm open source?: Yes - both are open-source projects on GitHub (llama.cpp: MIT, airllm: Apache-2.0).
Where can I find alternatives to llama.cpp or airllm?: GraphCanon lists graph-backed alternatives at /tools/ggml-org-llama-cpp/alternatives and /tools/lyogavin-airllm/alternatives (/tools/ggml-org-llama-cpp/alternatives.md, /tools/lyogavin-airllm/alternatives.md), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?: Yes. The markdown twin at /compare/ggml-org-llama-cpp-vs-lyogavin-airllm.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, llama.cpp or airllm?: llama.cpp: Very active. airllm: 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 airllm?: GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama.cpp: /tools/ggml-org-llama-cpp/trust; airllm: /tools/lyogavin-airllm/trust.