For the fastest local setup of this model, Docker is the best choice.
Follow the guidelines below to continue.
The client handles the setup, pulling gigabytes of data automatically.
During setup, the script automatically determines and applies the best settings tailored to your machine.
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 |
- Script automating parallel down-streaming of sharded Hugging Face model chunks
- SmolLM3-3B Zero Config FREE
- Installer configuring multi-user access permissions for local Ollama nodes
- SmolLM3-3B Uncensored Edition Local Guide FREE
- Installer pre-configuring Qwen2.5-Coder models for offline IDE plugins
- How to Launch SmolLM3-3B PC with NPU
- Script downloading localized multi-language LLM checkpoints directly
- SmolLM3-3B Windows 10 Complete Walkthrough FREE
- Downloader pulling specialized offline translation models for LibreTranslate nodes
- Install SmolLM3-3B 100% Private PC
- Downloader pulling micro-parameter language files for instantaneous automated replies
- SmolLM3-3B on Your PC Offline Setup FREE