For the fastest local setup of this model, enabling Windows Features is best.
Simply follow the directions outlined below.
1-click setup: the app automatically fetches the large weight files.
The smart installation system will instantly find the perfect 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 |
- Setup utility for loading Llama-3.3 high-context models into LM Studio
- How to Run SmolLM3-3B Locally (No Cloud) Local Guide FREE
- Setup script for single-click local LLM environment deployment
- SmolLM3-3B via WebGPU (Browser)
- Downloader pulling optimized coding assistants for offline development
- SmolLM3-3B PC with NPU For Low VRAM (6GB/8GB) Easy Build
- Downloader pulling specialized offline translation models for LibreTranslate nodes
- How to Setup SmolLM3-3B Uncensored Edition Local Guide FREE
- Setup utility automating memory-mapped file tweaks for massive model weights
- SmolLM3-3B No Admin Rights Easy Build FREE
- Downloader pulling extremely light gemma-2b profiles for real-time edge processing responses smoothly
- Run SmolLM3-3B Offline on PC Full Speed NPU Mode FREE