google/gemma-3-12b

gemma3 · 12B · 4bit

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 3, 2026

Top 12% Compare

Global Score

77 /100

Good

Hardware Fit

72/100

Quality

79/100

Get this model

🦙

Ollama

ollama pull google/gemma-3-12b

View on Ollama Library

ollama.com/library/google/gemma-3-12b

🤗

Find on HuggingFace

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: 7.9
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.3
Time to first token How fast the model starts responding — lower is better: 1.8 s
Load time Time to load the model into memory before inference: 0.0 s
Memory usage RAM consumed during inference — percentage of total system memory: 0.0 GB (0%)
Total tokens Total number of tokens generated across all benchmark prompts: 994

Score breakdown

Speed

14/40

Time to first token

28/30

Memory

30/30

Quality

Reasoning

13/20

Coding

19/20

Instruction following

14/20

Structured output

15/15

Math

8/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.0
Runtime: Lm-studio unknown
Model format: GGUF
Hardware profile: BALANCED
Result hash: b7b9b2bdd13a2a6450631a5c39b97bb03b48c0aea24b40c51d79489a8c72a338

Interpretation

Hardware fit: 72/100. Overall suitability: GOOD (Global 77/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Adequate, Multilingual: Strong.

Run yours now

npx metrillm@latest bench

Requires Node 20+ and Ollama or LM Studio running