Alibaba/Dense

AlibabaQwen3 0.6B

Qwen3 0.6B — latest architecture with reasoning support at minimal size.

chatreasoningThinkingTool Use
0.6B
Parameters
32K
Context length
19
Benchmarks
6
Quantizations
200K
HF downloads
Architecture
Dense
Released
2025-04-28
Layers
28
KV Heads
8
Head Dim
128
Family
qwen

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.890.9 GBgood
Q5_K_S5.570.9 GBgood
Q5_K_M5.70.9 GBgood
Q6_K6.561.0 GBexcellent
Q8_08.51.1 GBlossless
FP16161.7 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN QWEN3 0.6B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (19)

MATH65.0
HumanEval62.0
IFEval60.0
MATH-50052.1
GPQA Diamond23.1
IFBench21.9
τ²-Bench14.6
AIME10.3
AA Math10.3
BigCodeBench8.8
LiveCodeBench7.3
AA Intelligence5.7
HLE5.2
BBH5.1
MMLU-PRO4.6
SciCode4.1
MUSR2.0
AA Coding1.4
GPQA0.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3:0.6b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Find the best GPU for Qwen3 0.6B

Build Hardware for Qwen3 0.6B

Qwen3 0.6B — latest architecture with reasoning support at minimal size.

▸ SPEC SHEET

Qwen3 0.6B0.6B Dense.

▸ SPECIFICATIONS
PARAMETERS
0.6B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, reasoning
RELEASE DATE
2025-04-28
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.890.9 GB94%
Q5_K_S5.570.9 GB96%
Q5_K_M5.70.9 GB96%
Q6_K6.561.0 GB97%
Q8_08.51.1 GB100%
FP16161.7 GB100%
§ 01BENCHMARK SCORES
HumanEval62.0
MMLU-PRO4.6
MATH65.0
IFEval60.0
BBH5.1
GPQA0.0
MUSR2.0
BigCodeBench8.8
GPQA Diamond23.1
LiveCodeBench7.3
AIME10.3
MATH-50052.1
HLE5.2
AA Intelligence5.7
AA Coding1.4
AA Math10.3
aa_ifbench21.9
aa_tau214.6
aa_scicode4.1
§ 02RUN COMMAND

Run Qwen3 0.6B locally with Ollama — needs 0.9 GB VRAM at Q4_K_M:

$ollama run qwen3:0.6b