Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- energy-efficiency
+- green-ai
+- quantization
+- rtx-5090
+- benchmark
+- llm
+---
+
+# 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
+
+![Energy Comparison](fig1_energy_comparison.pdf)
+![Energy Trend](fig2_energy_trend.pdf)
+![Power Throughput](fig3_power_throughput.pdf)
+
+## 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}
+}