qwen2.5-coder:32b

qwen2 · 32.8B · Q4_K_M

NVIDIA NVIDIA_DGX_Spark (Cortex-X925)

122 GB · Ubuntu 24.04.4 LTS

Tested on June 26, 2026

Top 33% Compare

Global Score

78 /100

Good

Hardware Fit

59/100

Quality

86/100

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Ollama

ollama pull qwen2.5-coder:32b

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ollama.com/library/qwen2.5-coder

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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 24.04.4 LTS
Arch: arm64
Power Mode: performance

Performance

Tokens/sec Tokens generated per second — higher is better: 9.5
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: 394 ms
Time to first token How fast the model starts responding — lower is better: 395 ms
Load time Time to load the model into memory before inference: 72.1 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: 1222

Score breakdown

Speed

9/50

Time to first token

20/20

Memory

30/30

Quality

Reasoning

14/20

Coding

19/20

Instruction following

17/20

Structured output

15/15

Math

11/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama 0.30.10
Model format: GGUF
Hardware profile: HIGH-END
Result hash: 7cd4f1652964c0b655892cb228bb168ab9a05e2f0d6fd3c200f2b9e651771328

Interpretation

Hardware fit: 59/100. Overall suitability: GOOD (Global 78/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Strong, Multilingual: Strong.

Bench Environment

Thermal: nominal Swap delta: +0.3 GB CPU load: avg 15% (peak 32%)

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$ metrillm

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Or run without installing: npx metrillm@latest