hongpingzhang/rtx5090-energy-benchmark

RTX 5090 LLM Energy Benchmark

First energy efficiency benchmark of 4-bit quantization on NVIDIA RTX 5090 (Blackwell architecture).

Key Finding

4-bit quantization increases energy consumption by up to 29% for models < 5B parameters.

The crossover point where quantization becomes beneficial is ~5B parameters.

Results

Model	FP16 Energy	4-bit Energy	Change
TinyLlama 1.1B	1,659 J/1k	2,098 J/1k	+26.5% 🔴
Qwen2 1.5B	2,411 J/1k	3,120 J/1k	+29.4% 🔴
Qwen2.5 3B	3,383 J/1k	3,780 J/1k	+11.7% 🔴
Qwen2 7B	5,509 J/1k	4,878 J/1k	-11.4% 🟢

Figures

Hardware

• GPU: NVIDIA GeForce RTX 5090 (Blackwell, sm_120)
• VRAM: 32 GB GDDR7
• PyTorch: 2.10.0+cu128

Citation

@misc{rtx5090benchmark2026, title={When Quantization Hurts: Energy Efficiency on RTX 5090}, author={hongpingzhang}, year={2026}, url={https://huggingface.co/datasets/hongpingzhang/rtx5090-energy-benchmark} }