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| title: README | |
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| [Inference Snaps](https://github.com/canonical/inference-snaps) turn complex generative model deployment into a single command. | |
| They are built on the idea that AI runtimes should be portable, composable, and local-first. | |
| Whether you’re experimenting with Gemma 4, Qwen 3 Coder, or DeepSeek R1, you can instantly install a pre-optimized runtime and spin up an API endpoint locally. | |
| Each model snap includes all dependencies and is tuned for your silicon: CPU, GPU, or NPU. | |
| The [Inference Snaps Hugging Face organization](https://huggingface.co/inference-snaps) is used to maintain the model weights going into Inference Snaps. | |
| The weights are typically split into small shards of 5GB or less. |