SmolLM3-3B
Q4 GGUF download: bartowski/HuggingFaceTB_SmolLM3-3B-GGUF - 1.92 GB on disk, read 2026-10-10. VRAM figures above are estimates; this is the actual file size.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve HuggingFaceTB/SmolLM3-3BNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Frequently asked
Quick answers to common questions
How much VRAM does SmolLM3-3B need?
SmolLM3-3B with 3.1B parameters needs approximately 2 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is SmolLM3-3B better than other HuggingFaceTB models?
SmolLM3-3B has 3.1B parameters with 65,536 context - a strong choice for general use.
What license is SmolLM3-3B under?
SmolLM3-3B is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs SmolLM3-3B well?
With 3.1B parameters, SmolLM3-3B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for SmolLM3-3B?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~2 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can SmolLM3-3B's context window handle?
SmolLM3-3B supports a 65,536-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.
What models compete with SmolLM3-3B?
SmolLM3-3B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec