mlx-community/Llama-3.2-3B-Instruct-4bit

MacBook Air (Apple M4)

32 GB · macOS 26.3

Tested on March 5, 2026

Top 33% Compare

Global Score

74 /100

Good

Hardware Fit

100/100

Quality

63/100

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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: 50.6
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.5
First chunk latency Delay before the first streamed chunk arrives from runtime: 257 ms
Time to first token How fast the model starts responding — lower is better: 257 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: 0.7 GB (2%)
Total tokens Total number of tokens generated across all benchmark prompts: 1427

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

9/20

Coding

14/20

Instruction following

14/20

Structured output

15/15

Math

3/15

Multilingual

8/10

Category levels

Reasoning: Weak 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: GGUF
Hardware profile: BALANCED
Result hash: 4a3ab480438e07e208b775cf45889d7f8c32319eedf52fa190aa6270975f88ae

Interpretation

Hardware fit: 100/100. Overall suitability: GOOD (Global 74/100). Category profile: Reasoning: Weak, Coding: Adequate, Instruction Following: Adequate, Structured Output: Strong, Math: Poor, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 22% (peak 26%)

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