SmolLM3-3B Using Pinokio with Native FP4
Deploying this model locally is quickest when done via Docker.
Use the instructions provided below to complete the setup.
The loader auto-caches the model archive (several GBs included).
You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.
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 local guardrail models for filtering bad responses
- How to Run SmolLM3-3B via WebGPU (Browser) Zero Config Full Method FREE
- Setup utility linking custom local LLM pipelines with federated LibreChat workspace grids
- Setup SmolLM3-3B PC with NPU
- Downloader pulling specialized network security log parsing local setups
- SmolLM3-3B PC with NPU Offline Setup
