The fastest method for installing this model locally is by using Docker.

Follow the step-by-step instructions below.

The loader auto-caches the model archive (several GBs included).

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🛡️ Checksum: a9d5b91237ba2faff7c8e3d4761e66a6 — ⏰ Updated on: 2026-06-23 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: high-speed DDR5 memory preferred for CPU offloading Storage:100 GB free space for HuggingFace cache folder 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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