airllm vs ollama
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| airllm | ollama | |
|---|---|---|
| Tagline | Repository for running large language models with reduced memory usage on limited GPU hardware. | Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. |
| Stars | 22k | 176k |
| Forks | 2.6k | 17k |
| Open issues | 106 | 3.4k |
| Language | Jupyter Notebook | Go |
| License | Apache-2.0 | MIT |
| Last pushed | Jul 7, 2026 | Jul 7, 2026 |
| Categories | Model Training, Inference & Serving | AI Agents, LLM Frameworks |
airllm
AirLLM allows efficient inference of large language models like 70B parameter sizes using only a single 4GB GPU, without applying techniques such as quantization, distillation, or pruning. It supports various large-scale models and enhances performance capabilities through continuous updates focusing on model optimizations.
Jupyter Notebook
ollama
Ollama is a platform for deploying and interacting with various large language models (LLMs) such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, and Gemma on macOS, Windows, Linux, and Docker environments.
Go