mlx-community/meta-llama-3.1-8b-instruct

llama · 8B · 4bit

Mac mini (Apple M4 Pro)

64 GB · macOS 15.7.4

Tested on March 5, 2026

Top 24% Compare

Global Score

77 /100

Good

Hardware Fit

100/100

Quality

67/100

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Hardware

Machine: Mac mini
CPU: Apple M4 Pro
Cores: 14 total (10 perf + 4 eff)
Frequency: 2.4 GHz
RAM: 64 GB LPDDR5
GPU: Apple M4 Pro
OS: macOS 15.7.4
Arch: arm64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 54.3
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.8
First chunk latency Delay before the first streamed chunk arrives from runtime: 277 ms
Time to first token How fast the model starts responding — lower is better: 277 ms
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 4.2 GB (7%)
Total tokens Total number of tokens generated across all benchmark prompts: 1269

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

11/20

Coding

14/20

Instruction following

14/20

Structured output

15/15

Math

4/15

Multilingual

9/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.6+1
Model format: MLX
Hardware profile: HIGH-END
Result hash: d4961db8b5159f438a53459029328cddcd8de35810e985c4939fdf31326588a4

Interpretation

Hardware fit: 100/100. Overall suitability: GOOD (Global 77/100). Category profile: Reasoning: Adequate, Coding: Adequate, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 25% (peak 28%)

Run yours now

$ npm install -g metrillm@latest

$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest