smollm2-360m-instruct

llama · 360M · bf16

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 4, 2026
Top 80% Compare
Global Score
52 /100
Marginal
Hardware Fit
100/100
Quality
32/100

Get this model

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
$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest