smollm2-360m-instruct
llama · 360M · bf16
MacBook Air (Apple M4)
32 GB · macOS 26.3
Tested on March 4, 2026
Global Score
52 /100
Marginal
Hardware Fit
100/100
Quality
32/100
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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
- 121.1
- Standard deviation
- ±4.3
- First chunk latency
- 106 ms
- Time to first token
- 106 ms
- Load time
- N/A
- Memory usage
- 0.7 GB (2%)
- Total tokens
- 1046
Score breakdown
Speed
50/50
Time to first token
20/20
Memory
30/30
Quality
Reasoning
4/20
Coding
3/20
Instruction following
12/20
Structured output
7/15
Math
2/15
Multilingual
4/10
Category levels
Reasoning: Poor Coding: Poor Instruction Following: Adequate Structured Output: Weak Math: Poor Multilingual: Weak
Metadata
- Spec version
- 0.2.1
- Runtime
- LM Studio 0.4.6+1
- Model format
- MLX
- Hardware profile
- BALANCED
- Result hash
- 9f84d8c5aa42f7c68c37b26feea98fff35f4e370e050754dfbab379c7f7f6157
Interpretation
Hardware fit: 100/100. Overall suitability: MARGINAL (Global 52/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Adequate, Structured Output: Weak, Math: Poor, Multilingual: Weak.
Bench Environment
Power: AC CPU load: avg 15% (peak 17%)
Run yours now
$
npm install -g metrillm@latest$
metrillmRequires Node 20+ and Ollama or LM Studio running
Or run without installing: npx metrillm@latest