hf.co/openbmb/MiniCPM5-1B-GGUF:F16

llama · 1.08B · F16

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 23, 2026

Top 76% Compare

Global Score

62 /100

Good

Hardware Fit

100/100

Quality

45/100

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Ollama

ollama pull hf.co/openbmb/MiniCPM5-1B-GGUF:F16

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ollama.com/library/hf.co/openbmb/MiniCPM5-1B-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: 88.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.6
First chunk latency Delay before the first streamed chunk arrives from runtime: 36 ms
Time to first token How fast the model starts responding — lower is better: 36 ms
Load time Time to load the model into memory before inference: 1.6 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: 1398

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

16/20

Coding

9/20

Instruction following

4/20

Structured output

0/15

Math

14/15

Multilingual

2/10

Category levels

Reasoning: Strong Coding: Weak 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: 8fe504f3da09a77b4d281c9dcc93abca626264f66bcf91fd60e71c9a2f883a81

Interpretation

Hardware fit: 100/100. Overall suitability: GOOD (Global 62/100). Category profile: Reasoning: Strong, Coding: Weak, Instruction Following: Poor, Structured Output: Poor, Math: Strong, Multilingual: Poor.

Bench Environment

Power: AC CPU load: avg 56% (peak 66%)

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