We have data on the environmental impact per AI prompt:
Gemini: 0.00024 kWh & 0.26 mL water
ChatGPT: 0.0003 kWh & 0.38 mL
…the same energy as one Google search in 2008 & 6 drops of water. Seems to be improving, too: Google reports a 33x drop in energy use per prompt in a year.
COMPUTING
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AI Model Energy Efficiency Improves 33x Year-over-Year
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Think SMART Framework Balances AI Accuracy Latency ROI
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The 𝗧𝗵𝗶𝗻𝗸 𝗦𝗠𝗔𝗥𝗧 framework helps enterprises strike the right balance of accuracy, latency and ROI when deploying AI at AI factory scale. Learn more: https://
nvda.ws/41MTqTc -
Blackwell Delivers 4x Performance and 10x Profit Gains
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𝗥eturn On Investment driven by performance Performance per watt per $ = profit. Moving from Hopper to Blackwell delivers 4× more perf and up to 10× profit within the same power budget.
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Open Models Power 70% of AI Inference Workloads
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𝗧echnology ecosystem and install base Open models power over 70% of AI Inference workloads. NVIDIA supports 1,000+ OSS projects and 450+ open models like Llama, Gemma, and GPT-OSS, and collaborates on frameworks like @GoogleDeepMind JAX, @PyTorch
, @lmsysorg (SGLang), -
NVIDIA Blackwell GB200 NVL72 Delivers 50× AI Productivity Gains
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𝗔rchitecture and software The NVIDIA Blackwell platform + GB200 NVL72 rack system = up to 50× higher AI factory productivity. Throughput + energy and water efficiency gains + full-stack orchestration = scalable inference.
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Enterprise AI Infrastructure: Scaling Models and Inference Demands
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𝗦cale and complexity Bigger models = greater inference. From quick queries to million-token reasoning, infra demands during inference are soaring. Enterprises are building new AI factories with partners like @CoreWeave
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NVIDIA Inference Platform: Balancing Accuracy, Latency, and Cost
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𝗠ulti-dimensional performance Optimal Inference is a trade-off: accuracy, latency, and cost. Some tasks need ultra-low latency (real-time translation), while others prioritize throughput (multi-million-token queries). The NVIDIA Inference Platform accelerates models
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Smart Inference Deployment: Scaling AI Across Enterprise Systems
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Deploying AI at scale requires you to 𝗧𝗵𝗶𝗻𝗸 𝗦𝗠𝗔𝗥𝗧 about inference: 𝗦cale and complexity 𝗠ulti-dimensional performance
𝗔rchitecture & software
𝗥OI driven by performance
𝗧echnology ecosystem & install base Let’s break it down -
NVIDIA Blackwell GB200 NVL72 50x AI Inference Productivity
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𝗔rchitecture and software The NVIDIA Blackwell platform + GB200 NVL72 rack system = up to 50× higher AI factory productivity. Throughput + energy and water efficiency gains + full-stack orchestration = scalable inference.
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Blackwell delivers 4x performance within same power budget
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𝗥eturn On Investment driven by performance Performance per watt per $ = profit. Moving from Hopper to Blackwell delivers 4× more perf and up to 10× profit within the same power budget.