If you need a near-instant local setup, just fetch files via a basic curl request.
Use the instructions provided below to complete the setup.
The script takes care of fetching the multi-gigabyte model weights.
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 |
- Downloader pulling compact 2-bit quantization variants for rapid text prototyping
- Deploy SmolLM3-3B For Low VRAM (6GB/8GB) Dummy Proof Guide FREE
- Installer deploying local internet-free web scraping tools with built-in vision parsing tasks
- Run SmolLM3-3B Offline on PC Direct EXE Setup
- Installer setting up SillyTavern interface optimized for KoboldCPP 2.10+ processing backends
- SmolLM3-3B No Python Required Offline Setup
- Script configuring localized DeepSeek-R1-Distill-Llama models for terminal inference
- SmolLM3-3B with Native FP4 Windows
