microllm-50m:latest

llama · 49.31M · F16

Mac mini (Apple M4 Pro)

24 GB · macOS 15.6.1

Tested on June 28, 2026 · Submitted by Tram

Top 97% Compare

Global Score

28 /100

Not Rec.

Hardware Fit

80/100

Quality

5/100

Get this model

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Ollama

ollama pull microllm-50m:latest

View on Ollama Library

ollama.com/library/microllm-50m

Get it in LM Studio

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Find on HuggingFace

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: 1000000.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.0
First chunk latency Delay before the first streamed chunk arrives from runtime: 50 ms
Time to first token How fast the model starts responding — lower is better: 30.0 s
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: 0.1 GB (1%)
Total tokens Total number of tokens generated across all benchmark prompts: 121

Score breakdown

Speed

50/50

Time to first token

0/20

Memory

30/30

Quality

Reasoning

0/20

Coding

0/20

Instruction following

5/20

Structured output

0/15

Math

0/15

Multilingual

0/10

Category levels

Reasoning: Poor Coding: Poor Instruction Following: Weak 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: 9f2435ad5707c03931b5ba1119c76a47394180fca6c0d3aaf2896646189e4e50

Interpretation

Hardware fit: 80/100. Overall suitability: NOT RECOMMENDED (Global 28/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Weak, 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.

Disqualifiers

Time to first token too high: 30000ms (maximum: 20636ms for BALANCED profile)

Bench Environment

Power: AC CPU load: avg 10% (peak 17%)

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