maternion/ling-3.0-tiny:8b

bailingmoe3 · 7.9B · Q4_K_M

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 24, 2026 · Submitted by AAA

Top 65% Compare

Global Score

68 /100

Good

Hardware Fit

98/100

Quality

55/100

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Ollama

ollama pull maternion/ling-3.0-tiny:8b

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ollama.com/library/maternion/ling-3.0-tiny

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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: 73.6
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: 190 ms
Time to first token How fast the model starts responding — lower is better: 190 ms
Load time Time to load the model into memory before inference: 2.9 s
Memory usage RAM consumed during inference — percentage of total system memory: 5.9 GB (37%)
Total tokens Total number of tokens generated across all benchmark prompts: 1452

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

28/30

Quality

Reasoning

19/20

Coding

17/20

Instruction following

3/20

Structured output

0/15

Math

15/15

Multilingual

1/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: d08742bd77e3f3d48e6be528b19ef5bd108a93b661e18eb044ac22433016d36a

Interpretation

Hardware fit: 98/100. Overall suitability: GOOD (Global 68/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Poor, Structured Output: Poor, Math: Strong, Multilingual: Poor.

Bench Environment

Power: AC CPU load: avg 43% (peak 49%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest