Qwen 3 8B

Overview

Alibaba's 8B dense model with a toggleable thinking mode and broad multilingual coverage. Punches well above its weight for an 8B and runs comfortably on a single consumer GPU.

When to pick this model

• You want one local 8B that handles both quick chat and harder reasoning via a thinking toggle
• You need multilingual coverage across 100+ languages without paying API fees
• You're prototyping agents and want long context (up to 131K with YaRN) on modest hardware
• You need an Apache 2.0 model for commercial deployment at the 8B tier

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	5 GB
Q5_K_M	6 GB
Q8_0	9 GB
FP16 (no quantization)	16 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-Pro	68.7
GPQA	60
LiveCodeBench	54.4

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

Strengths

• Hybrid thinking/fast modes switchable per request
• Strong multilingual performance across 119 languages
• Up to 131K context via YaRN (32K native)
• Apache 2.0 — clean commercial use

Limitations

• Thinking traces are verbose and burn tokens fast
• Ecosystem tooling still less mature than the Qwen 2.5 line

Architecture & training

Architecture: Dense · GQA · hybrid thinking/non-thinking mode

Training: 36T tokens, 119 languages.

Quick start

ollama run qwen3:8b

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