lmstudio-community/gemma-4-e4b-it

gemma4 · Q4_K_M

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

15 GB · Ubuntu 26.04 LTS

Tested on June 29, 2026

Top 39% Compare

Global Score

76 /100

Good

Hardware Fit

67/100

Quality

80/100

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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: 10.8
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
First chunk latency Delay before the first streamed chunk arrives from runtime: 44 ms
Time to first token How fast the model starts responding — lower is better: 2.0 s
Load time Time to load the model into memory before inference: 10.9 s
Memory usage RAM consumed during inference — percentage of total system memory: 0.0 GB (0%)
Total tokens Total number of tokens generated across all benchmark prompts: 1363

Score breakdown

Speed

20/50

Time to first token

17/20

Memory

30/30

Quality

Reasoning

14/20

Coding

19/20

Instruction following

16/20

Structured output

15/15

Math

6/15

Multilingual

10/10

Category levels

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

Metadata

Spec version: 0.2.1
Runtime: LM Studio 0.4.16+1
Model format: GGUF
Hardware profile: BALANCED
Result hash: 9e3bacabc62b3ea257538163c337f5561d2ba0671b3fbbe6d5f676ff18fbedfd

Interpretation

Hardware fit: 67/100. Overall suitability: GOOD (Global 76/100). Category profile: Reasoning: Adequate, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Bench Environment

Thermal: moderate CPU load: avg 57% (peak 58%)

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$ metrillm

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Or run without installing: npx metrillm@latest