smollm2:135m

llama · 134.52M · F16

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 1, 2026

Top 89% Compare

Global Score

48 /100

Marginal

Hardware Fit

100/100

Quality

14/100

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Ollama

ollama pull smollm2:135m

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ollama.com/library/smollm2

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GGUF versions & quantizations

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 Tokens generated per second — higher is better: 245.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±1.4
Time to first token How fast the model starts responding — lower is better: 31 ms
Load time Time to load the model into memory before inference: 0.4 s
Memory usage RAM consumed during inference — percentage of total system memory: 0.6 GB (2%)
Total tokens Total number of tokens generated across all benchmark prompts: 1158

Score breakdown

Speed

40/40

Time to first token

30/30

Memory

30/30

Quality

Reasoning

4/20

Coding

0/20

Instruction following

6/20

Structured output

2/15

Math

1/15

Multilingual

1/10

Category levels

Reasoning: Poor Coding: Poor Instruction Following: Weak Structured Output: Poor Math: Poor Multilingual: Poor

Metadata

Spec version: 0.2.0
Runtime: Ollama 0.17.4
Model format: GGUF
Hardware profile: BALANCED
Result hash: d9d63279a3f4f8fde4845a7e9637c757cf931aad0375afcbd77714dba724b0a5

Interpretation

Hardware fit: 100/100. Overall suitability: MARGINAL (Global 48/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Weak, 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.

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npx metrillm@latest bench

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