Using Docker is the absolute quickest way to install this model on your local machine.
Follow the guidelines below to continue.
The installer auto-downloads and deploys the entire model pack.
The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.
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 advanced upscaler model weights like SUPIR-v2 for Forge workflows
- How to Deploy SmolLM3-3B 100% Private PC One-Click Setup 5-Minute Setup
- Setup utility enabling modern multi-head attention acceleration keys for host machines hardware rigs
- Launch SmolLM3-3B Zero Config Windows
- Setup tool initializing prefix-caching parameters inside production-tier vLLM arrays
- How to Install SmolLM3-3B Dummy Proof Guide