qwen3-vl:4b-instruct

qwen3vl · 4.4B · Q4_K_M

Mac Studio (Apple M2 Max)

96 GB · macOS 26.1

Tested on April 20, 2026 · Submitted by Frank

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Global Score

83 /100

Excellent

Hardware Fit

96/100

Quality

78/100

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ollama pull qwen3-vl:4b-instruct

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GGUF versions & quantizations

Hardware

Machine: Mac Studio
CPU: Apple M2 Max
Cores: 12 total (8 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 96 GB LPDDR5
GPU: Apple M2 Max
OS: macOS 26.1
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 90.6
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.6
First chunk latency Delay before the first streamed chunk arrives from runtime: 159 ms
Time to first token How fast the model starts responding — lower is better: 159 ms
Load time Time to load the model into memory before inference: 5.8 s
Memory usage RAM consumed during inference — percentage of total system memory: 42.5 GB (44%)
Total tokens Total number of tokens generated across all benchmark prompts: 905

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

26/30

Quality

Reasoning

14/20

Coding

16/20

Instruction following

16/20

Structured output

15/15

Math

8/15

Multilingual

9/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.20.7
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 59702806fc2dee8832b77aa7c2af373b4363484bafa249826a0f5dabe67f70fd

Interpretation

Hardware fit: 96/100. Overall suitability: EXCELLENT (Global 83/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Adequate, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 9% (peak 10%)

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