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 25, 2026 · Submitted by AAA

Top 61% Compare

Global Score

71 /100

Good

Hardware Fit

100/100

Quality

58/100

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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: 88.1
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: 38 ms
Time to first token How fast the model starts responding — lower is better: 38 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: 1078

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

12/20

Coding

10/20

Instruction following

15/20

Structured output

11/15

Math

3/15

Multilingual

7/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.15
Model format: GGUF
Hardware profile: ENTRY
Result hash: 608bb0ea3f24c53bea92403da0c925263e169a3b6e2b2dac914af1461d1e9ba0

Interpretation

Hardware fit: 100/100. Overall suitability: GOOD (Global 71/100). Category profile: Reasoning: Adequate, Coding: Adequate, Instruction Following: Strong, Structured Output: Adequate, Math: Poor, Multilingual: Adequate.

Bench Environment

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

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