qwen3:8b

qwen3 · 8.2B · Q4_K_M

Micro-Star International Co., Ltd. Stealth GS77 12UH (Intel Gen Intel® Core™ i9-12900H)

32 GB · Microsoft Windows 11 Home 10.0.26200

Tested on October 31, 2026

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Global Score

83 /100

Excellent

Hardware Fit

100/100

Quality

75/100

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ollama pull qwen3:8b

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ollama.com/library/qwen3

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GGUF versions & quantizations

Hardware

Machine: Micro-Star International Co., Ltd. Stealth GS77 12UH
CPU: Intel Gen Intel® Core™ i9-12900H
Cores: 20 total (20 perf)
Frequency: 2.9 GHz
RAM: 32 GB DDR5
GPU: Intel(R) Iris(R) Xe Graphics, NVIDIA GeForce RTX 3080 Laptop GPU
OS: Microsoft Windows 11 Home 10.0.26200
Arch: x64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 54.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.9
First chunk latency Delay before the first streamed chunk arrives from runtime: 68 ms
Time to first token How fast the model starts responding — lower is better: 68 ms
Load time Time to load the model into memory before inference: 6.1 s
Memory usage RAM consumed during inference — percentage of total system memory: 5.8 GB (18%)
Total tokens Total number of tokens generated across all benchmark prompts: 894

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

15/20

Coding

15/20

Instruction following

15/20

Structured output

14/15

Math

6/15

Multilingual

10/10

Category levels

Reasoning: Strong Coding: Strong Instruction Following: Strong Structured Output: Strong Math: Weak Multilingual: Strong

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.40.13
Model format: GGUF
Hardware profile: BALANCED
Result hash: eb3322293d03b4a5d06ccc4542713b9f540a94b6b657335ad3f8e75127dcb5e9

Interpretation

Hardware fit: 100/100. Overall suitability: EXCELLENT (Global 83/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 34% (peak 69%)

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$ metrillm

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Or run without installing: npx metrillm@latest