To install this model locally in the shortest time, opt for a direct curl execution.
Refer to the action plan below to initialize the model.
The installer automatically pulls the model (could be multiple GBs).
To save you time, the system will automatically determine efficient resource allocation.
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 |
- Downloader pulling high-quality voice profiles for local Fish-Speech setups
- Quick Run SmolLM3-3B Offline on PC For Low VRAM (6GB/8GB) Direct EXE Setup
- Installer pre-configuring Automatic1111 WebUI extensions and dependencies
- Setup SmolLM3-3B Locally via LM Studio
- Installer deploying local bark audio generation pipelines with custom speaker tokens
- SmolLM3-3B Using Pinokio Local Guide