Qwen3.8-27B-UD-Q3_K_XL

llama · 27.3B · Q4_K

ECO MODE

Intel Gen Intel® Core™ i5-12600K

31 GB · Ubuntu 24.04.4 LTS

Tested on August 23, 2026

Top 6% Compare

Global Score

90 /100

Excellent

Hardware Fit

97/100

Quality

87/100

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Ollama

ollama pull Qwen3.8-27B-UD-Q3_K_XL

View on Ollama Library

ollama.com/library/Qwen3.8-27B-UD-Q3_K_XL

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GGUF versions & quantizations

Hardware

CPU: Intel Gen Intel® Core™ i5-12600K
Cores: 16 total (6 perf + 4 eff)
Frequency: 1.12 GHz
RAM: 31 GB
GPU: AlderLake-S GT1, AD106 [GeForce RTX 4060 Ti 16GB]
OS: Ubuntu 24.04.4 LTS
Arch: x64
Power Mode: low-power

Performance

Tokens/sec Tokens generated per second — higher is better: 39.0
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±3.8
First chunk latency Delay before the first streamed chunk arrives from runtime: 305 ms
Time to first token How fast the model starts responding — lower is better: 305 ms
Load time Time to load the model into memory before inference: 0.0 s
Memory usage RAM consumed during inference — percentage of total system memory: 12.5 GB (41%)
Total tokens Total number of tokens generated across all benchmark prompts: 1048

Score breakdown

Speed

50/50

Time to first token

20/20

Memory

27/30

Quality

Reasoning

17/20

Coding

19/20

Instruction following

15/20

Structured output

15/15

Math

11/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: Ollama llamacpp-shim-1.0
Model format: GGUF
Hardware profile: BALANCED
Result hash: ab563ee3bce9f17a0d579dd9587ccbccf0e7f1aa3c6f3faa0c9f2c379a9c18df

Interpretation

Hardware fit: 97/100. Overall suitability: EXCELLENT (Global 90/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Strong, Multilingual: Strong.

Warnings

System was in low-power mode during this benchmark.

Bench Environment

Thermal: nominal CPU load: avg 6% (peak 7%)

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$ metrillm

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Or run without installing: npx metrillm@latest