gemma4:12b

gemma4 · 11.9B · Q4_K_M

MacBook Pro (Apple M2 Pro)

16 GB · macOS 26.5

Tested on July 4, 2026 · Submitted by enigmatracer

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Global Score

89 /100

Excellent

Hardware Fit

88/100

Quality

89/100

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Ollama

ollama pull gemma4:12b

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ollama.com/library/gemma4

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GGUF versions & quantizations

Hardware

Machine: MacBook Pro
CPU: Apple M2 Pro
Cores: 10 total (6 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 16 GB LPDDR5
GPU: Apple M2 Pro
OS: macOS 26.5
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 18.4
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
First chunk latency Delay before the first streamed chunk arrives from runtime: 758 ms
Time to first token How fast the model starts responding — lower is better: 758 ms
Load time Time to load the model into memory before inference: 0.3 s
Memory usage RAM consumed during inference — percentage of total system memory: 7.5 GB (47%)
Total tokens Total number of tokens generated across all benchmark prompts: 1130

Score breakdown

Speed

42/50

Time to first token

20/20

Memory

26/30

Quality

Reasoning

17/20

Coding

18/20

Instruction following

17/20

Structured output

15/15

Math

12/15

Multilingual

10/10

Category levels

Reasoning: Strong Coding: Strong Instruction Following: Strong Structured Output: Strong Math: Strong Multilingual: Strong

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.31.1
Model format: GGUF
Hardware profile: ENTRY
Result hash: dc778c4aede91da89d9826a2a7ce1077ccd6364b6145cdf91422eaf13009b6a3

Interpretation

Hardware fit: 88/100. Overall suitability: EXCELLENT (Global 89/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Strong, Multilingual: Strong.

Warnings

Running on battery power — performance may be reduced.

Bench Environment

Power: Battery CPU load: avg 6% (peak 7%)

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