codellama:7b

llama · 7B · Q4_0

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 1, 2026

Top 49% Compare

Global Score

68 /100

Good

Hardware Fit

97/100

Quality

49/100

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ollama.com/library/codellama

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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: 23.6
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±1.3
Time to first token How fast the model starts responding — lower is better: 297 ms
Load time Time to load the model into memory before inference: 0.9 s
Memory usage RAM consumed during inference — percentage of total system memory: 12.6 GB (39%)
Total tokens Total number of tokens generated across all benchmark prompts: 1218

Score breakdown

Speed

37/40

Time to first token

30/30

Memory

30/30

Quality

Reasoning

6/20

Coding

7/20

Instruction following

12/20

Structured output

13/15

Math

3/15

Multilingual

8/10

Category levels

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

Metadata

Spec version: 0.2.0
Runtime: Ollama 0.17.4
Model format: GGUF
Hardware profile: BALANCED
Result hash: 8d0b5e31e6603a28a1c42a82a9649fa5a6c60762d2ab256eb86949d81854b7e9

Interpretation

Hardware fit: 97/100. Overall suitability: GOOD (Global 68/100). Category profile: Reasoning: Weak, Coding: Weak, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Strong.

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