Deploying this model locally is quickest when done via a simple curl command.
Make sure to follow the instructions below.
The script takes care of fetching the multi-gigabyte model weights.
Your resources are automatically evaluated to lock in the premium configuration.
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 |
- Installer configuring secure local graph databases to map model interaction memories
- SmolLM3-3B with 1M Context Windows
- Script downloading custom tokenizers optimized for highly non-English text
- How to Run SmolLM3-3B For Beginners
- Setup utility for loading Llama-3.3 high-context models into LM Studio
- How to Launch SmolLM3-3B FREE