arXiv AI By Emanuele Del Sozzo, Martin Fleming, Kenneth Flamm, Neil Thompson

How Much Progress Has There Been in NVIDIA Datacenter GPUs?

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arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.

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arXiv AI
Jul 3

The Rising Unsustainability of AI Graphics Cards Production

arXiv:2607. 01258v1 Announce Type: cross Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software.

By Cl\'ement Morand, Aur\'elie N\'ev\'eol, Anne-Laure Ligozat
arXiv AI
6d ago

Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware

The paper reports a reproducible GPU power benchmark for 18 open‑source LLMs (0.5B–7B parameters) run on a single consumer RTX 4060ti GPU using the Ollama inference engine. Energy metrics such as mean/peak power, total energy per prompt, energy per output token, and throughput were measured, revealing that model architecture and quantization strategy, rather than parameter count alone, drive energy efficiency. The most efficient models were qwen2.5:0.5b and tinyllama:1.1b, while the 7B‑Mistral model consumed up to 8.6× more energy per token, and qwen3.5:0.8b(on) showed unusually high per‑prompt energy due to extended internal reasoning.

By Philipp M. Z\"ahl, Elja Dalipaj, Anika Hennig, Timon Bayer