Homebrew offers the quickest path to setting up this model locally.
Please follow the instructions listed below to get started.
The loader auto-caches the model archive (several GBs included).
An automated hardware sweep ensures the system will select the best tuning parameters.
SmolLM3-3B is a compact language model designed for efficient inference on consumer hardware. It leverages a refined architecture that balances parameter count and context length, delivering strong performance in both reasoning and generation tasks. The model supports up to 8K tokens of context, enabling it to handle longer dialogues and documents without truncation. Benchmarks show it outperforms similarly sized models in multilingual understanding and code generation. Its training pipeline incorporates extensive data filtering and instruction tuning, resulting in coherent and factual outputs. The compact footprint makes it ideal for deployment in edge devices and research prototypes.
| Parameter | Value |
|---|---|
| Parameters | 3 B |
| Context Length | 8K tokens |
| Training Data | ≈1.5 TB filtered corpus |
| Inference Speed | ~120 tokens/s on GPU |
- Setup tool updating local miniconda environments for PyTorch 2.5+
- SmolLM3-3B Dummy Proof Guide
- Installer configuring localized web dashboards for Whisper-Large-V3 video transcription
- Install SmolLM3-3B Offline on PC Zero Config Local Guide FREE
- Installer configuring audio source separation setups for stem mastering
- Run SmolLM3-3B
