microllm-10m:latest

llama · 10.00M · F16

Mac mini (Apple M4 Pro)

24 GB · macOS 15.6.1

Tested on June 28, 2026 · Submitted by Tram

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Global Score

33 /100

Not Rec.

Hardware Fit

100/100

Quality

4/100

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Ollama

ollama pull microllm-10m:latest

View on Ollama Library

ollama.com/library/microllm-10m

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GGUF versions & quantizations

Hardware

Machine: Mac mini
CPU: Apple M4 Pro
Cores: 12 total (8 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 24 GB LPDDR5
GPU: Apple M4 Pro
OS: macOS 15.6.1
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 1544.8
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±310.8
First chunk latency Delay before the first streamed chunk arrives from runtime: 12 ms
Time to first token How fast the model starts responding — lower is better: 12 ms
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: 1440

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

0/20

Coding

0/20

Instruction following

4/20

Structured output

0/15

Math

0/15

Multilingual

0/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.30.11
Model format: GGUF
Hardware profile: BALANCED
Result hash: e6810869d0bd86f6df49bffb0da362830eca612e74ad8addb63c8a86e519a105

Interpretation

Hardware fit: 100/100. Overall suitability: NOT RECOMMENDED (Global 33/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Poor, Structured Output: Poor, Math: Poor, Multilingual: Poor. Warning: model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Warnings

Model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Bench Environment

Power: AC CPU load: avg 9% (peak 10%)

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