qwen3.6:35b-a3b

qwen35moe · 36.0B · Q4_K_M

ECO MODE

AZW GTR Pro (AMD RYZEN AI MAX+ 395)

125 GB · Ubuntu 24.04.4 LTS

Tested on April 17, 2026

Top 6% Compare

Global Score

88 /100

Excellent

Hardware Fit

91/100

Quality

87/100

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Ollama

ollama pull qwen3.6:35b-a3b

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ollama.com/library/qwen3.6

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GGUF versions & quantizations

Hardware

Machine: AZW GTR Pro
CPU: AMD RYZEN AI MAX+ 395
Cores: 32 threads (16 cores)
Frequency: 3 GHz
RAM: 125 GB
GPU: Radeon 8060S
OS: Ubuntu 24.04.4 LTS
Arch: x64
Power Mode: low-power

Performance

Tokens/sec Tokens generated per second — higher is better: 43.3
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.2
First chunk latency Delay before the first streamed chunk arrives from runtime: 291 ms
Time to first token How fast the model starts responding — lower is better: 291 ms
Load time Time to load the model into memory before inference: 6.2 s
Memory usage RAM consumed during inference — percentage of total system memory: 32.2 GB (26%)
Total tokens Total number of tokens generated across all benchmark prompts: 1024

Score breakdown

Speed

41/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

16/20

Coding

18/20

Instruction following

16/20

Structured output

15/15

Math

12/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.20.0
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 58ac1caf4620b2b0117d710ba4b8a80de81c39145489652455bfd2ee9b53bf04

Interpretation

Hardware fit: 91/100. Overall suitability: EXCELLENT (Global 88/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Strong, Multilingual: Strong.

Warnings

System was in low-power mode during this benchmark.
CPU appears throttled (2.1 GHz current vs 3.0 GHz nominal, 69%).

Bench Environment

Thermal: nominal CPU load: avg 5% (peak 7%)

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$ metrillm

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Or run without installing: npx metrillm@latest