Launch SmolLM3-3B Locally via Ollama 2

If you want the fastest local installation for this model, use Docker.

Refer to the instructions below to proceed.

The setup auto-streams the model assets (expect a multi-GB download).

The smart installation system will instantly find the perfect configuration for your specific hardware.

🖹 HASH-SUM: baad3a0b51288740ea76ba4fcb78e571 | 📅 Updated on: 2026-06-28 Verify CPU: multi-threading optimized for fast prompt processing RAM: 48 GB needed to prevent memory swapping to disk Disk: high-speed SSD 120 GB to cache model layers Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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.

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