Deploying this model locally is quickest when done via Docker.
Follow the sequence of steps detailed below.
The setup auto-streams the model assets (expect a multi-GB download).
The installer will automatically analyze your hardware and select the optimal configuration for your system.
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 |
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