meta-llama-3.1-8b-instruct

llama · 8B · 4bit

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 3, 2026

Top 17% Compare

Global Score

76 /100

Good

Hardware Fit

95/100

Quality

68/100

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Ollama

ollama pull meta-llama-3.1-8b-instruct

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ollama.com/library/meta-llama-3.1-8b-instruct

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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: 21.6
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.3
Time to first token How fast the model starts responding — lower is better: 512 ms
Load time Time to load the model into memory before inference: 0.0 s
Memory usage RAM consumed during inference — percentage of total system memory: 0.0 GB (0%)
Total tokens Total number of tokens generated across all benchmark prompts: 1228

Score breakdown

Speed

35/40

Time to first token

30/30

Memory

30/30

Quality

Reasoning

11/20

Coding

15/20

Instruction following

14/20

Structured output

14/15

Math

4/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.0
Runtime: Lm-studio unknown
Model format: GGUF
Hardware profile: BALANCED
Result hash: 017a6b62a9b9fd0b1ade26008465d911d127db6191b4ecceb5af8d1729355ad0

Interpretation

Hardware fit: 95/100. Overall suitability: GOOD (Global 76/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Strong.

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