smollm2-1.7b-instruct

llama · 1.7B · bf16

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 4, 2026

Top 55% Compare

Global Score

65 /100

Good

Hardware Fit

99/100

Quality

51/100

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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 Tokens generated per second — higher is better: 28.5
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.7
First chunk latency Delay before the first streamed chunk arrives from runtime: 393 ms
Time to first token How fast the model starts responding — lower is better: 393 ms
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 3.2 GB (10%)
Total tokens Total number of tokens generated across all benchmark prompts: 1114

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

29/30

Quality

Reasoning

10/20

Coding

10/20

Instruction following

13/20

Structured output

11/15

Math

2/15

Multilingual

5/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.6+1
Model format: MLX
Hardware profile: BALANCED
Result hash: ec25f40c1e2070835141eac4bf7124b69ffc5e0e99609d00c1d328b3755b9430

Interpretation

Hardware fit: 99/100. Overall suitability: GOOD (Global 65/100). Category profile: Reasoning: Weak, Coding: Adequate, Instruction Following: Adequate, Structured Output: Adequate, Math: Poor, Multilingual: Adequate.

Bench Environment

Power: AC CPU load: avg 13% (peak 15%)

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$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest