SmolLM3-3B

The fastest tactical way to launch this model locally is via a Docker image.

Execute the commands and steps outlined below.

The setup auto-downloads all needed files (several GBs).

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

💾 File hash: 96f10b69284337cedc4e35026736ca6e (Update date: 2026-07-02)
  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Storage: extra room for future model updates and datasets
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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