phi-3-mini-128k-instruct

phi3 · 4bit

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 4, 2026
Top 93% Compare
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%)

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