qwen3-vl-4b-instruct-q4_k_m.gguf

Mac Studio (Apple M2 Max)

96 GB · macOS 26.1

Tested on April 21, 2026 · Submitted by Frank
Top 67% Compare
Global Score
62 /100
Good
Hardware Fit
100/100
Quality
46/100

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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
67.8
Standard deviation
±27.6
First chunk latency
62 ms
Time to first token
64 ms
Load time
0.0 s
Memory usage
0.0 GB (0%)
Total tokens
983

Score breakdown

Speed
50/50
Time to first token
20/20
Memory
30/30

Quality

Reasoning
16/20
Coding
0/20
Instruction following
12/20
Structured output
1/15
Math
8/15
Multilingual
9/10

Category levels

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

Metadata

Spec version
0.2.1
Runtime
Openai compatible
Model format
GGUF
Hardware profile
HIGH-END
Result hash
4b6bf2b5fcf2e091947c886e36a6e9b3fd230c5d6a947b0807a7d2522d14dd0f

Interpretation

Hardware fit: 100/100. Overall suitability: GOOD (Global 62/100). Category profile: Reasoning: Strong, Coding: Poor, Instruction Following: Adequate, Structured Output: Poor, Math: Adequate, Multilingual: Strong.

Warnings

  • Token speed is unstable (stddev 27.6 tok/s, mean 67.8 tok/s) — may indicate thermal throttling or memory pressure.

Bench Environment

Power: AC CPU load: avg 7% (peak 8%)

Run yours now

$ npm install -g metrillm@latest
$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest