hf.co/unsloth/Qwen3.8-27B-GGUF:UD-IQ1_S

qwen35 · 27.3B · unknown

MacBook Pro (Apple M1 Pro)

16 GB · macOS 26.6.2

Tested on August 25, 2026 · Submitted by AAA
Top 90% Compare
Global Score
46 /100
Marginal
Hardware Fit
50/100
Quality
44/100

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Hardware

Machine
MacBook Pro
CPU
Apple M1 Pro
Cores
10 total (8 perf + 2 eff)
Frequency
2.4 GHz
RAM
16 GB LPDDR5
GPU
Apple M1 Pro
OS
macOS 26.6.2
Arch
arm64
Power Mode
balanced

Performance

Tokens/sec
6.0
Standard deviation
±399997.6
First chunk latency
2.4 s
Time to first token
2.2 s
Load time
6.8 s
Memory usage
10.6 GB (66%)
Total tokens
726

Score breakdown

Speed
15/50
Time to first token
18/20
Memory
17/30

Quality

Reasoning
8/20
Coding
5/20
Instruction following
10/20
Structured output
11/15
Math
3/15
Multilingual
7/10

Category levels

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

Metadata

Spec version
0.2.1
Runtime
Ollama 0.32.15
Model format
GGUF
Hardware profile
ENTRY
Result hash
5475f51f21418b6c0ce572c4000d3763a6373704d70f3946f9bc8326de72ce09

Interpretation

Hardware fit: 50/100. Overall suitability: MARGINAL (Global 46/100). Category profile: Reasoning: Weak, Coding: Weak, Instruction Following: Adequate, Structured Output: Adequate, Math: Poor, Multilingual: Adequate.

Warnings

  • Token speed is unstable (stddev 399997.6 tok/s, mean 6.0 tok/s) — may indicate thermal throttling or memory pressure.
  • Significant swap activity during benchmark (+1.7 GB). Model may exceed available RAM — results are severely degraded.

Bench Environment

Power: AC Swap delta: +1.7 GB CPU load: avg 36% (peak 47%)

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