unsloth/gemma-4-26b-a4b-it@q4_k_m

gemma4 · 26B · Q4_K_M

Docker Container (Intel Gen Intel® Core™ i9-11900H)

15 GB · Ubuntu 26.04 LTS

Tested on June 29, 2026

Top 17% Compare

Global Score

83 /100

Excellent

Hardware Fit

58/100

Quality

93/100

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Hardware

Machine: Docker Container
CPU: Intel Gen Intel® Core™ i9-11900H
Cores: 16 threads (8 cores)
Frequency: 2.5 GHz
RAM: 15 GB
GPU: TigerLake-H GT1 [UHD Graphics]
OS: Ubuntu 26.04 LTS
Arch: x64
Power Mode: performance

Performance

Tokens/sec Tokens generated per second — higher is better: 9.4
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.9
First chunk latency Delay before the first streamed chunk arrives from runtime: 42 ms
Time to first token How fast the model starts responding — lower is better: 4.1 s
Load time Time to load the model into memory before inference: 44.3 s
Memory usage RAM consumed during inference — percentage of total system memory: 0.2 GB (1%)
Total tokens Total number of tokens generated across all benchmark prompts: 1429

Score breakdown

Speed

17/50

Time to first token

11/20

Memory

30/30

Quality

Reasoning

18/20

Coding

19/20

Instruction following

18/20

Structured output

15/15

Math

13/15

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: LM Studio 0.4.16+1
Model format: GGUF
Hardware profile: BALANCED
Result hash: cf859c53d7de98f2ea9c97c60fbee89cb6147d898b4ef529e492b0fefb11d944

Interpretation

Hardware fit: 58/100. Overall suitability: EXCELLENT (Global 83/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Strong, Multilingual: Strong.

Warnings

Significant swap activity during benchmark (+5.2 GB). Model may exceed available RAM — results are severely degraded.

Bench Environment

Thermal: nominal Swap delta: +5.2 GB CPU load: avg 52% (peak 53%)

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$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest