hf.co/bartowski/ai9stars_G9v3-3B-GGUF:BF16

llama · 2.99B · BF16

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 22, 2026

Top 72% Compare

Global Score

65 /100

Good

Hardware Fit

94/100

Quality

52/100

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Ollama

ollama pull hf.co/bartowski/ai9stars_G9v3-3B-GGUF:BF16

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ollama.com/library/hf.co/bartowski/ai9stars_G9v3-3B-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: 29.6
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: 264 ms
Time to first token How fast the model starts responding — lower is better: 264 ms
Load time Time to load the model into memory before inference: 3.8 s
Memory usage RAM consumed during inference — percentage of total system memory: 8.4 GB (53%)
Total tokens Total number of tokens generated across all benchmark prompts: 1349

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

24/30

Quality

Reasoning

17/20

Coding

14/20

Instruction following

6/20

Structured output

0/15

Math

14/15

Multilingual

1/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.32.14
Model format: GGUF
Hardware profile: ENTRY
Result hash: 88bb8ae9972d4061c821262fe6a309b11861919f1e7d21d4b97bb935f9523300

Interpretation

Hardware fit: 94/100. Overall suitability: GOOD (Global 65/100). Category profile: Reasoning: Strong, Coding: Adequate, Instruction Following: Weak, Structured Output: Poor, Math: Strong, Multilingual: Poor.

Bench Environment

Power: AC Swap delta: +0.3 GB CPU load: avg 26% (peak 28%)

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