smollm2:135m

llama · 134.52M · F16

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 2, 2026

Top 92% Compare

Global Score

41 /100

Marginal

Hardware Fit

100/100

Quality

15/100

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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: 241.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±24.7
First chunk latency Delay before the first streamed chunk arrives from runtime: N/A
Time to first token How fast the model starts responding — lower is better: 39 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

40/50

Time to first token

30/20

Memory

30/30

Quality

Reasoning

4/20

Coding

1/20

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: 1d78f4b3198b90954635edd7aeddc82a55079718b8500c88c2452a451e077304

Interpretation

Hardware fit: 100/100. Overall suitability: MARGINAL (Global 49/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Weak, Structured Output: Poor, Math: Poor, Multilingual: Poor.

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