SmolLM3 3B

Overview

HuggingFace's 3B model with dual think/no-think modes, 128k context, and full open data and recipe — punching at MMLU 59.7 and GSM8K 70.9.

When to pick this model

• Edge devices and laptops needing real reasoning at 3B
• Long-context tasks where larger models aren't viable
• Multilingual chat across the six supported European languages
• Educational and research use needing fully open training
• Latency-sensitive applications wanting toggleable thinking

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	2 GB
Q5_K_M	2.5 GB
Q8_0	4 GB
FP16 (no quantization)	6 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
MMLU	59.7
GSM8K	70.9

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• Compact think mode delivers reasoning at 3B scale
• Native support for six languages
• Apache 2.0 with fully open training data and recipe
• 128k context unusual at this size
• Strong MMLU and GSM8K for the parameter count

Limitations

• No official Ollama distribution — needs manual setup
• Quality ceiling typical of 3B dense models on hard tasks
• Smaller community than competing 3B releases

Architecture & training

Architecture: Dense 3B · dual-mode think/no-think · 64k native + YaRN

Training: Fully open (data + recipe).

Quick start

# HuggingFace : HuggingFaceTB/SmolLM3-3B

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.