unsloth-phi-4

llama · 4bit

Mac mini (Apple M4 Pro)

64 GB · macOS 15.7.4

Tested on March 6, 2026

Top 13% Compare

Global Score

82 /100

Excellent

Hardware Fit

88/100

Quality

80/100

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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: 27.4
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.7
First chunk latency Delay before the first streamed chunk arrives from runtime: 457 ms
Time to first token How fast the model starts responding — lower is better: 457 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: 7.7 GB (12%)
Total tokens Total number of tokens generated across all benchmark prompts: 1297

Score breakdown

Speed

38/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

15/20

Coding

19/20

Instruction following

14/20

Structured output

13/15

Math

9/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.6+1
Model format: MLX
Hardware profile: HIGH-END
Result hash: 113833d6a8d151f7b1d2cf97ddae996b3c0304e5797c1463f050c363f2e57f25

Interpretation

Hardware fit: 88/100. Overall suitability: EXCELLENT (Global 82/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Adequate, Structured Output: Strong, Math: Adequate, Multilingual: Strong.

Bench Environment

Power: AC CPU load: avg 25% (peak 27%)

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