temp rename to force case-sensitive change

Author: AseelOmer

temp_models/models.md

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+# Model 1: GPT-4 (OpenAI)
+
+## Model Name and Provider
+
+**GPT-4**, developed by **OpenAI**.
+
+### Hosting and Deployment
+
+Hosted via **Microsoft Azure OpenAI Service**.
+
+Source: [Azure blog – Introducing GPT-4 in Azure OpenAI Service][azure-blog].
+
+Cloud infrastructure uses global data centers; regions are not public.
+
+### Estimated Energy (Inference)
+
+Published or estimated per-query energy values vary between studies.
+Representative numbers include:
+
+**Epoch AI (2024):** ≈ 0.3 Wh (0.0003 kWh) per ChatGPT/GPT-4 query.
+
+Source: [Epoch AI – How Much Energy Does ChatGPT Use?][epoch-ai].
+
+Other analysts estimate ≈ 0.3 – 1.8 Wh (0.0003 – 0.0018 kWh)
+depending on prompt length, token output, and GPU hardware.
+
+Sources: “The Carbon Footprint of ChatGPT,” media analyses.
+
+**Caveat:** OpenAI does not publish per-query energy data.  
+All estimates depend on assumptions such as:
+
+* Hardware type (GPU vs TPU)
+* Power Usage Effectiveness (PUE)
+* Data center region and carbon intensity
+* Prompt and token length
+
+### Training Energy (GPT-4)
+
+Some analyses extrapolate GPT-4’s training energy from model size and
+compute budget:
+
+≈ 51 – 62 GWh (51 772 500 – 62 318 750 kWh) for full-scale training.
+
+Source: [The Carbon Footprint of ChatGPT][sustainability-numbers].
+
+These are indirect estimates, not official OpenAI disclosures.
+
+### Water Usage (GPT-4)
+
+Official data are unavailable, but media analyses suggest:
+
+A single ChatGPT query may indirectly consume ≈ 0.5 L of water,
+depending on data-center cooling.
+
+Generating a 100-word email may use ≈ 0.14 kWh energy and 0.52 L water.
+
+Source: [The Verge – Sam Altman on ChatGPT Energy and Water Use][verge-gpt].
+
+### PUE and CI Context (GPT-4)
+
+Studies multiply compute energy by:
+
+* **PUE** – Power Usage Effectiveness (total facility power / IT power)
+* **CI** – Carbon Intensity (kg CO₂e / kWh electricity)
+
+Example assumptions:
+
+* **PUE:** ≈ 1.1 – 1.3 for Azure hyperscale centers  
+* **CI:** ≈ 0.3 – 0.4 kg CO₂e / kWh (depending on region)
+
+---
+
+## Model 2: Claude Haiku (Anthropic)
+
+### Model Name & Provider
+
+**Claude 3 Haiku**, developed by **Anthropic**.
+
+### Model Description
+
+Part of Anthropic’s Claude 3 family (Haiku, Sonnet, Opus).  
+Released March 2024. Smallest and fastest model for low-latency,
+energy-efficient inference in chat, summarization, and automation.
+
+Source: [Anthropic Blog – Claude 3 Technical Overview][anthropic-blog].
+
+### Hosting & Deployment
+
+Hosted via Anthropic API and **Amazon Bedrock (AWS)**.
+These centers maintain **PUE ≈ 1.2**.
+
+Sources: [AWS Bedrock Claude Integration], [AWS Sustainability Report 2024][aws-report].
+
+### Estimated Energy
+
+Anthropic does not publish per-query energy data.
+Independent analysts estimate ≈ 0.05 – 0.1 Wh (0.00005 – 0.0001 kWh)
+per query based on token count and GPU efficiency.
+
+Claude 3 Haiku is ≈ 5× faster and more efficient than larger Claude 3 models.
+
+Sources: [Epoch AI – Energy Use of AI Models][epoch-ai],
+[Anthropic Claude 3 Announcement].
+
+### Training Energy
+
+Claude 3 models use NVIDIA A100/H100 GPUs on AWS.
+Typical energy use ≈ 3 000 – 10 000 MWh for 10–30 B parameters.
+
+Sources: [Epoch AI – AI Training Compute and Energy Scaling],
+[Anthropic Responsible Scaling Policy][anthropic-policy].
+
+### Water Usage
+
+No specific data published.  
+Cooling water managed under **AWS sustainability strategy**.  
+Cooler regions use air cooling; others recycle water on-site.
+
+Sources: [AWS Water Stewardship Report][aws-water],
+[Anthropic Sustainability Commitments].
+
+### PUE and CI Context
+
+* **AWS PUE:** ≈ 1.2  
+* **Carbon Intensity:** ≈ 0 – 0.2 kg CO₂e / kWh (depending on renewables)  
+AWS targets 100 % renewable energy by 2025.
+
+Sources: [AWS Global Infrastructure Efficiency Data],
+[Anthropic Responsible Scaling Policy][anthropic-policy].
+
+---
+
+## Model 3: Gemini Nano (Google)
+
+### Provider
+
+**Gemini Nano**, developed by **Google DeepMind**.  
+Smallest member of Gemini family (Nano, Pro, Ultra).
+
+### Hosting
+
+Runs on-device via **Android AICore** (subsystem introduced 2023).  
+Designed for mobile hardware like Pixel 8 Pro and Pixel 9.  
+Reduces energy use by eliminating cloud compute and network load.
+
+Sources: [Google AI Blog – Introducing Gemini][google-blog],
+[Android Developers – Gemini Nano Overview][android-dev],
+[The Verge – Gemini Nano on Pixel 8 Pro][verge-gemini].
+
+### Estimated Energy(Inference)
+
+No official values.  
+Device benchmarks show ≈ 0.01 Wh (0.00001 kWh) per query —
+10 – 30× more efficient than GPT-4.
+
+Sources: [Google Pixel AI Benchmarks (2024)],
+[Epoch AI – How Much Energy Does ChatGPT Use][epoch-ai].
+
+### Training Energy of gemini
+
+Gemini Nano is distilled from larger Gemini models trained on **TPU v5e**.  
+Training energy ≈ 200 – 1 200 MWh (1 – 5 % of Gemini Ultra).
+
+Sources: [Google Research – Efficient TPU Training (2024)],
+[Google Cloud Sustainability Report (2024)].
+
+### Water Usage (nano)
+
+Inference uses no data-center water.  
+Training used Google data centers with **WUE ≈ 0.18 L/kWh**.  
+Google targets net-positive water impact by 2030.
+
+Sources: [Google Environmental Report (2024)],
+[Bloomberg – Google AI Water Consumption (2024)].
+
+### PUE & CI Context
+
+* **PUE:** ≈ 1.10 – 1.12 (Google Data Centers)
+* **CI:** ≈ 0.15 kg CO₂e / kWh (70 % renewable mix)
+* **On-device:** < 5 W per inference
+
+Sources: [Google Data Center Efficiency Overview (2024)],
+[Google TPU v5e Efficiency Blog (2024)].
+
+---
+
+[azure-blog]:
+https://azure.microsoft.com/en-us/blog/introducing-gpt4-in-azure-openai-service/
+[epoch-ai]:
+https://epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use
+[sustainability-numbers]:
+https://www.sustainabilitybynumbers.com/p/carbon-footprint-chatgpt
+[verge-gpt]:
+https://www.theverge.com/2023/1/18/energy-water-chatgpt
+[anthropic-blog]:
+https://www.anthropic.com/blog/claude3-overview
+[aws-report]:
+https://aws.amazon.com/about-aws/sustainability/
+[anthropic-policy]:
+https://www.anthropic.com/responsible-scaling
+[aws-water]:
+https://aws.amazon.com/about-aws/sustainability/#water
+[google-blog]:
+https://blog.google/technology/ai/google-gemini-ai/
+[android-dev]:
+https://developer.android.com/ai/gemini-nano
+[verge-gemini]:
+https://www.theverge.com/2023/12/6/23990823/google-gemini-ai-models-nano-pro-ultra
+[AWS Bedrock Claude Integration]:
+https://aws.amazon.com/bedrock/
+[Anthropic Claude 3 Announcement]:
+https://www.anthropic.com/news/claude-3-models
+[Epoch AI – AI Training Compute and Energy Scaling]:
+https://epoch.ai/gradient-updates/ai-training-compute-energy-scaling
+[Anthropic Sustainability Commitments]:
+https://www.anthropic.com/sustainability
+[AWS Global Infrastructure Efficiency Data]:
+https://aws.amazon.com/about-aws/sustainability/
+[Google Pixel AI Benchmarks (2024)]:
+https://ai.google/discover/pixel-ai/
+[Google Research – Efficient TPU Training (2024)]:
+https://arxiv.org/abs/2408.15734
+[Google Cloud Sustainability Report (2024)]:
+https://sustainability.google/reports/environmental-report-2024/
+[Bloomberg – Google AI Water Consumption (2024)]:
+https://www.bloomberg.com/news/articles/2024-02-13/google-ai-water-consumption-analysis
+[Google Data Center Efficiency Overview (2024)]:
+https://cloud.google.com/sustainability/data-centers
+[Google TPU v5e Efficiency Blog (2024)]:
+https://cloud.google.com/blog/products/ai-machine-learning/introducing-tpu-v5e