hf.co/lmstudio-community/gemma-4-E4B-it-GGUF:Q6_K

gemma4 · 7.52B · unknown

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 23, 2026 · Submitted by AAA

Top 13% Compare

Global Score

88 /100

Excellent

Hardware Fit

100/100

Quality

83/100

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ollama pull hf.co/lmstudio-community/gemma-4-E4B-it-GGUF:Q6_K

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ollama.com/library/hf.co/lmstudio-community/gemma-4-E4B-it-GGUF

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GGUF versions & quantizations

Hardware

Machine: MacBook Pro
CPU: Apple M1 Pro
Cores: 10 total (8 perf + 2 eff)
Frequency: 2.4 GHz
RAM: 16 GB LPDDR5
GPU: Apple M1 Pro
OS: macOS 26.6.2
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 34.6
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.2
First chunk latency Delay before the first streamed chunk arrives from runtime: 210 ms
Time to first token How fast the model starts responding — lower is better: 210 ms
Load time Time to load the model into memory before inference: 4.0 s
Memory usage RAM consumed during inference — percentage of total system memory: 3.9 GB (25%)
Total tokens Total number of tokens generated across all benchmark prompts: 1083

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

14/20

Coding

18/20

Instruction following

17/20

Structured output

15/15

Math

9/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: d45939a43f73f6cc401c4f621e71b488bbf10541a2d5a9963c160c77d05557c6

Interpretation

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

Bench Environment

Power: AC CPU load: avg 26% (peak 30%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest