vicuna:7b

llama · 7B · Q4_0

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 1, 2026

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Global Score

65 /100

Good

Hardware Fit

97/100

Quality

43/100

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Ollama

ollama pull vicuna:7b

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ollama.com/library/vicuna

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GGUF versions & quantizations

Hardware

Machine: MacBook Air
CPU: Apple M4
Cores: 10 total (4 perf + 6 eff)
Frequency: 2.4 GHz
RAM: 32 GB LPDDR5
GPU: Apple M4
OS: macOS 26.3
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 23.4
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.6
Time to first token How fast the model starts responding — lower is better: 242 ms
Load time Time to load the model into memory before inference: 0.4 s
Memory usage RAM consumed during inference — percentage of total system memory: 5.8 GB (18%)
Total tokens Total number of tokens generated across all benchmark prompts: 1165

Score breakdown

Speed

37/40

Time to first token

30/30

Memory

30/30

Quality

Reasoning

8/20

Coding

4/20

Instruction following

10/20

Structured output

12/15

Math

2/15

Multilingual

7/10

Category levels

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

Metadata

Spec version: 0.2.0
Runtime: Ollama 0.17.4
Model format: GGUF
Hardware profile: BALANCED
Result hash: acd74b64d4870038e30d7454ea716fac1c65c78d279279de22167929350f2ad4

Interpretation

Hardware fit: 97/100. Overall suitability: GOOD (Global 65/100). Category profile: Reasoning: Weak, Coding: Poor, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Adequate.

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