hf.co/mradermacher/Phr00tyMix-v4-32B-i1-GGUF:Q4_K_M

qwen2 · 32.8B · Q4_K_M

NVIDIA NVIDIA_DGX_Spark (Cortex-X925)

122 GB · Ubuntu 24.04.5 LTS

Tested on October 2, 2026 · Submitted by Tram

Top 99% Compare

Global Score

18 /100

Not Rec.

Hardware Fit

60/100

Quality

0/100

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Ollama

ollama pull hf.co/mradermacher/Phr00tyMix-v4-32B-i1-GGUF:Q4_K_M

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ollama.com/library/hf.co/mradermacher/Phr00tyMix-v4-32B-i1-GGUF

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GGUF versions & quantizations

Hardware

Machine: NVIDIA NVIDIA_DGX_Spark
CPU: Cortex-X925
Cores: 20 total (20 perf)
Frequency: 3.35 GHz
RAM: 122 GB
GPU: Device 2e12
OS: Ubuntu 24.04.5 LTS
Arch: arm64
Power Mode: performance

Performance

Tokens/sec Tokens generated per second — higher is better: 10.8
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.0
First chunk latency Delay before the first streamed chunk arrives from runtime: 103 ms
Time to first token How fast the model starts responding — lower is better: 103 ms
Load time Time to load the model into memory before inference: 6.3 s
Memory usage RAM consumed during inference — percentage of total system memory: 26.5 GB (22%)
Total tokens Total number of tokens generated across all benchmark prompts: 1285

Score breakdown

Speed

10/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

0/20

Coding

0/20

Instruction following

0/20

Structured output

0/15

Math

0/15

Multilingual

0/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.35.0
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 656b1e67ba415542802deaf3d13bb45b98a6873a48b94cd6e8c911f8d9bc1f12

Interpretation

Hardware fit: 60/100. Overall suitability: NOT RECOMMENDED (Global 18/100). Category profile: Reasoning: Poor, Coding: Poor, Instruction Following: Poor, Structured Output: Poor, Math: Poor, Multilingual: Poor. Warning: model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Warnings

Model produced very low accuracy on quality tasks — results may be unusable despite good hardware performance.

Bench Environment

Thermal: nominal CPU load: avg 10% (peak 11%)

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$ npm install -g metrillm@latest

$ metrillm

Requires Node 20+ and Ollama or LM Studio running

Or run without installing: npx metrillm@latest