phi-3-mini-128k-instruct
phi3 · 4bit
MacBook Air (Apple M4)
32 GB · macOS 26.3
Tested on March 4, 2026
Global Score
38 /100
Not Rec.
Hardware Fit
100/100
Quality
12/100
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Hardware
- Machine
- MacBook Air
- CPU
- Apple M4
- Cores
- 10 total (4 perf + 6 eff)
- Frequency
- 2.4 GHz
- RAM
- 32 GB LPDDR5
- GPU
- Apple M4
- OS
- macOS 26.3
- Arch
- arm64
- Power Mode
- balanced
Performance
- Tokens/sec
- 44.0
- Standard deviation
- ±41.7
- First chunk latency
- 384 ms
- Time to first token
- 385 ms
- Load time
- N/A
- Memory usage
- 2.0 GB (6%)
- Total tokens
- 1014
Score breakdown
Speed
50/50
Time to first token
20/20
Memory
30/30
Quality
Reasoning
3/20
Coding
2/20
Instruction following
2/20
Structured output
1/15
Math
2/15
Multilingual
2/10
Category levels
Reasoning: Poor Coding: Poor Instruction Following: Poor Structured Output: Poor Math: Poor Multilingual: Poor
Metadata
- Spec version
- 0.2.1
- Runtime
- LM Studio 0.4.6+1
- Model format
- MLX
- Hardware profile
- BALANCED
- Result hash
- 1974f2fead57bf5f257a3f233fa160e4ec3879298f9b1058cd7088a640a0d79a
Interpretation
Hardware fit: 100/100. Overall suitability: NOT RECOMMENDED (Global 38/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Poor, Structured Output: Poor, Math: Poor, Multilingual: Poor. Warning: model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.
Warnings
- Model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.
- Token speed is unstable (stddev 41.7 tok/s, mean 44.0 tok/s) — may indicate thermal throttling or memory pressure.
Bench Environment
Power: AC CPU load: avg 17% (peak 25%)
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npm install -g metrillm@latest$
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Or run without installing: npx metrillm@latest