{"id":3991,"date":"2025-10-01T19:36:31","date_gmt":"2025-10-01T19:36:31","guid":{"rendered":"https:\/\/godofprompt.io\/blog\/2025\/10\/01\/how-to-benchmark-ai-models-for-energy-efficiency\/"},"modified":"2025-10-01T19:36:31","modified_gmt":"2025-10-01T19:36:31","slug":"how-to-benchmark-ai-models-for-energy-efficiency","status":"publish","type":"post","link":"https:\/\/godofprompt.ai\/blog\/how-to-benchmark-ai-models-for-energy-efficiency\/","title":{"rendered":"How to Benchmark AI Models for Energy Efficiency"},"content":{"rendered":"<p><strong>AI models consume significant energy, making efficiency a key concern for both costs and environmental impact.<\/strong> Training large models like <a href=\"https:\/\/en.wikipedia.org\/wiki\/GPT-3\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">GPT-3<\/a> can use as much electricity as 100 U.S. homes in a year, and data centers already account for up to 2% of global energy demand. By 2030, this could rise to 21%. Benchmarking is a practical way to measure and improve energy use in AI systems.<\/p>\n<h3 id=\"key-takeaways\" tabindex=\"-1\">Key Takeaways:<\/h3>\n<ul>\n<li><strong>Why It Matters<\/strong>: AI energy use impacts budgets and contributes to carbon emissions. For example, a single <a href=\"https:\/\/openai.com\/chatgpt\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">ChatGPT<\/a> query uses enough energy to power a 5-watt LED bulb for 1 hour and 20 minutes.<\/li>\n<li><strong>Benchmarking Tools<\/strong>: Tools like <a href=\"https:\/\/codecarbon.io\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">CodeCarbon<\/a> and <a href=\"https:\/\/github.com\/SotaroKaneda\/MLCarbon\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">MLCarbon<\/a> track energy use during training and inference. <a href=\"https:\/\/www.salesforce.com\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Salesforce<\/a>&#8216;s AI Energy Score is a new standard for comparing model efficiency.<\/li>\n<li><strong>Optimization Strategies<\/strong>: Techniques like quantization, pruning, and knowledge distillation can reduce energy use by up to 80% with minimal performance loss.<\/li>\n<li><strong>Standardized Testing<\/strong>: Use consistent hardware (e.g., <a href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/h100\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">NVIDIA H100<\/a> GPUs), datasets, and controlled environments for reliable results.<\/li>\n<li><strong>Metrics<\/strong>: Focus on energy per inference, watt-hours per 1,000 queries, and composite metrics like Energy Delay Product (EDP) to balance efficiency and speed.<\/li>\n<\/ul>\n<p>By measuring energy use and applying these strategies, businesses can cut costs, meet sustainability goals, and improve AI performance. Benchmarking and optimization aren&#8217;t just good practice &#8211; they&#8217;re necessary for scaling AI responsibly.<\/p>\n<h2 id=\"how-hungry-is-ai-benchmarking-energy-water-and-carbon-footprint-of-llm-inference\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference<\/h2>\n<p><iframe class=\"sb-iframe\" src=\"https:\/\/www.youtube.com\/embed\/wn2TwhGOGvI\" frameborder=\"0\" loading=\"lazy\" allowfullscreen style=\"width: 100%; height: auto; aspect-ratio: 16\/9;\"><\/iframe><\/p>\n<h2 id=\"tools-and-requirements-for-energy-benchmarking\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Tools and Requirements for Energy Benchmarking<\/h2>\n<p>Getting energy benchmarking right means using the right mix of hardware, software, and controlled environments. Without the proper tools, your results can be wildly inaccurate &#8211; sometimes overestimating energy usage by as much as 4.1 times the actual consumption. Precision here isn&#8217;t optional; it&#8217;s essential, and it hinges on having specialized hardware and purpose-built software.<\/p>\n<h3 id=\"hardware-and-software-essentials\" tabindex=\"-1\">Hardware and Software Essentials<\/h3>\n<p>Reliable energy benchmarking requires specialized AI hardware. GPUs are the backbone, consuming 50\u201370% of the total power provisioned in data centers running machine learning tasks. For standardized benchmarking, the <strong>NVIDIA H100 GPU<\/strong> with 80GB memory is the go-to option. On the other hand, the <strong><a href=\"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/40-series\/rtx-4090\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">RTX 4090<\/a><\/strong> with 24GB memory is ideal for single consumer GPU setups, while the <strong><a href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/a100\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">A100 GPU<\/a><\/strong> with 40GB memory is frequently used in research environments  . Standardizing hardware &#8211; like using identical NVIDIA H100 GPUs &#8211; removes variability and ensures consistent results across models.<\/p>\n<p>For edge AI applications, processors like <strong>Intel&#8217;s Core Ultra Series<\/strong> with integrated NPUs and iGPUs can handle tasks like single video streams at 30 fps. But when the workload ramps up, such as exceeding 120 fps, discrete GPUs like the <strong><a href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/rtx-4000-sff\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">NVIDIA RTX 4000 Ada SFF<\/a><\/strong> step in.<\/p>\n<p>On the software side, tools are critical for real-time energy tracking. <strong>CodeCarbon<\/strong> monitors energy consumption across CPU, GPU, and RAM during inference, while <strong><a href=\"https:\/\/ml.energy\/zeus\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Zeus<\/a><\/strong>, an open-source tool from the University of Michigan, specializes in measuring and optimizing energy use for deep learning tasks on both NVIDIA and AMD GPUs . To streamline the benchmarking process, the <strong><a href=\"https:\/\/github.com\/huggingface\/optimum-benchmark\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Optimum Benchmark<\/a> package<\/strong> integrates with these tools, delivering detailed performance and efficiency metrics.<\/p>\n<p>High-performance memory is another must-have. A minimum of 8GB is required, but 16GB is recommended for larger models. High-bandwidth memory ensures smooth data transfers between processing units.<\/p>\n<p>Beyond hardware and software, consistent benchmarking also depends on standardized datasets and controlled environments.<\/p>\n<h3 id=\"standard-datasets-and-testing-environments\" tabindex=\"-1\">Standard Datasets and Testing Environments<\/h3>\n<p>Controlled environments are key to producing reliable results. Effective benchmarking efforts often use custom datasets that reflect real-world usage by sampling from well-known sources.<\/p>\n<p>For instance, the <strong>AI Energy Score<\/strong> method creates datasets with 1,000 data points for each task, pulling from three respected datasets per category. Here are some examples:<\/p>\n<figure class=\"table\" style=\"width: 100%;max-width: 100%;overflow-x: scroll;\">\n<table>\n<thead>\n<tr>\n<th>Task Category<\/th>\n<th>Standard Datasets Used<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Text Generation<\/strong><\/td>\n<td><a href=\"https:\/\/huggingface.co\/datasets\/Salesforce\/wikitext\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">WikiText<\/a>, <a href=\"https:\/\/oscar-project.org\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">OSCAR<\/a>, <a href=\"https:\/\/github.com\/thunlp\/UltraChat\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">UltraChat<\/a><\/td>\n<\/tr>\n<tr>\n<td><strong>Summarization<\/strong><\/td>\n<td><a href=\"https:\/\/www.tensorflow.org\/datasets\/catalog\/cnn_dailymail\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">CNN Daily Mail<\/a>, SamSUM, ArXiv<\/td>\n<\/tr>\n<tr>\n<td><strong>Image Classification<\/strong><\/td>\n<td><a href=\"https:\/\/www.image-net.org\/challenges\/LSVRC\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">ImageNet ILSVRC<\/a>, Food 101, Bean Disease Dataset<\/td>\n<\/tr>\n<tr>\n<td><strong>Object Detection<\/strong><\/td>\n<td><a href=\"https:\/\/cocodataset.org\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">COCO 2017<\/a>, Visual Genome, Plastic in River<\/td>\n<\/tr>\n<tr>\n<td><strong>Speech Recognition<\/strong><\/td>\n<td><a href=\"https:\/\/www.openslr.org\/12\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">LibriSpeech<\/a>, <a href=\"https:\/\/commonvoice.mozilla.org\/datasets\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Common Voice<\/a>, People&#8217;s Speech<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>To ensure consistency, the AI Energy Score uses FP32 precision for most tasks but switches to FP16 for text generation, allowing better GPU resource management for larger models. A batch size of 1 is standard, and each model is tested ten times on its dataset to ensure statistically reliable results.<\/p>\n<p>Meanwhile, the <strong>ML.ENERGY Benchmark<\/strong> takes a different approach. It processes batches of 500 prompts from larger datasets and focuses on production-grade setups. Benchmarks are run on NVIDIA A100 and H100 GPUs, capturing steady-state energy consumption during extended deployments .<\/p>\n<p>Reproducible environments are critical for consistent results. Both major benchmarking initiatives rely on cloud-based standardized instances. For example, the ML.ENERGY Benchmark uses <a href=\"https:\/\/aws.amazon.com\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">AWS<\/a> <strong>p4d.24xlarge<\/strong> and <strong>p5.48xlarge<\/strong> instances, which minimize variability caused by factors like cooling systems, power delivery, and background processes. These environments ensure consistent energy measurements across runs. Additionally, secure containerized setups allow organizations to benchmark proprietary models while safeguarding intellectual property. Validation scripts further ensure proper GPU utilization during benchmarking.<\/p>\n<h2 id=\"step-by-step-energy-benchmarking-process\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Step-by-Step Energy Benchmarking Process<\/h2>\n<p>To benchmark the energy use of AI models effectively, you\u2019ll need to follow a structured process. This involves three key phases: preparation, measurement, and reporting. Each phase requires careful attention to ensure your results are accurate and align with industry standards. Using the hardware and dataset guidelines mentioned earlier, here\u2019s how to execute the benchmarking process.<\/p>\n<h3 id=\"preparing-for-benchmarking\" tabindex=\"-1\">Preparing for Benchmarking<\/h3>\n<p>Start by defining the tasks and methodology you\u2019ll use to ensure your results are meaningful.<\/p>\n<p><strong>Define Your AI Tasks.<\/strong> Focus on widely used machine learning tasks that reflect practical applications across various domains. The AI Energy Score project, introduced at the AI Action Summit in Paris in February 2025, provides a good starting point with standard tasks like text generation, image classification, object detection, summarization, speech-to-text, image generation, and image captioning.<\/p>\n<p><strong>Create Representative Datasets.<\/strong> For each task, build a dataset by sampling equally from established sources like WikiText, OSCAR, and UltraChat for text generation. This approach minimizes the risk of training data contamination.<\/p>\n<blockquote>\n<p>&quot;The goal of AI Energy Score is to establish a standardized approach for evaluating the energy efficiency of AI model inference. By focusing on controlled and comparable metrics, such as specific tasks and hardware, we aim to provide useful insights for researchers, developers, organizations, and policymakers.&quot; <\/p>\n<\/blockquote>\n<p><strong>Configure Your Models Consistently.<\/strong><\/p>\n<ul>\n<li>Stick to default precision settings (e.g., FP32 or FP16 for text generation) to ensure fair testing.<\/li>\n<li>Use a <strong>batch size of 1<\/strong> to maintain uniform workload conditions.<\/li>\n<li>Test models with their default configurations to mimic real-world production scenarios.<\/li>\n<\/ul>\n<p>For text generation tasks, group models based on their hardware needs &#8211; whether they\u2019re optimized for single consumer GPUs, single cloud GPUs, or multiple cloud GPUs. This classification ensures fair comparisons. Additionally, consider containerized testing to protect proprietary models.<\/p>\n<h3 id=\"measuring-energy-consumption\" tabindex=\"-1\">Measuring Energy Consumption<\/h3>\n<p>Once your setup is ready, the next step is to measure energy use. This starts with establishing a baseline to isolate the energy consumed by your AI workloads.<\/p>\n<p><strong>Focus on GPU Energy Use.<\/strong> GPUs often account for about half of a server&#8217;s total energy demand during AI tasks, making it essential to monitor their power draw. Tools like <strong>nvidia-smi<\/strong>, integrated with CodeCarbon, can track GPU energy consumption in real time.<\/p>\n<p><strong>Track Energy Across Inference Phases.<\/strong> Modern tools let you measure energy use during different stages, such as preprocessing, prefill, and decoding. Summing these values gives you the total energy consumed. To reduce variability, run each model 10 times and use the average.<\/p>\n<p>For smaller models, <strong>CodeCarbon<\/strong> is a reliable tool as it monitors energy use across the CPU, GPU, and RAM. However, ensure your workloads run for at least 5 minutes to avoid errors like &quot;No emissions data recorded.&quot; For larger language models, <strong>MLCarbon<\/strong> offers more comprehensive tracking, covering the full lifecycle &#8211; training, inference, and storage.<\/p>\n<p><strong>Monitor Total System Energy.<\/strong> To estimate total server energy, double the measured GPU energy consumption, as GPUs typically account for roughly half of a server&#8217;s energy use. Once you\u2019ve collected this data, you can move on to recording and standardizing your results.<\/p>\n<h3 id=\"recording-and-reporting-results\" tabindex=\"-1\">Recording and Reporting Results<\/h3>\n<p>Raw energy data needs to be normalized and presented in a clear, comparable format.<\/p>\n<p><strong>Standardize Results in Watt-Hours per 1,000 Queries.<\/strong> This metric allows for fair comparisons across different models, regardless of their absolute power consumption.<\/p>\n<p><strong>Convert Energy to Familiar Units.<\/strong> Express energy in <strong>kilowatt-hours (kWh)<\/strong> and calculate carbon emissions using U.S. carbon intensity figures. For example, the average in 2024 was 402.49 grams of CO\u2082 equivalent per kWh.<\/p>\n<p><strong>Relate kWh Values to Everyday Contexts:<\/strong><\/p>\n<figure class=\"table\" style=\"width: 100%;max-width: 100%;overflow-x: scroll;\">\n<table>\n<thead>\n<tr>\n<th>Energy Context<\/th>\n<th>Conversion Factor<\/th>\n<th>Example<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Microwave Operation<\/strong><\/td>\n<td>1 kWh = 1 hour at 1,000W<\/td>\n<td>0.5 kWh = 30 minutes<\/td>\n<\/tr>\n<tr>\n<td><strong>E-bike Range<\/strong><\/td>\n<td>1 kWh = 20\u201340 miles<\/td>\n<td>0.1 kWh = 2\u20134 miles<\/td>\n<\/tr>\n<tr>\n<td><strong>LED Bulb (10W)<\/strong><\/td>\n<td>1 kWh = 100 hours<\/td>\n<td>0.01 kWh = 1 hour<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p><strong>Document Your Methodology.<\/strong> Record all key variables, such as hardware specs (e.g., NVIDIA H100 GPUs), model precision settings, quantization configurations, and batching strategies. This ensures others can reproduce your results and understand any limitations.<\/p>\n<p><strong>Implement a Rating System.<\/strong> Alongside raw energy data, use a 1-to-5 star rating system (with 5 stars for the most energy-efficient models) to provide an easy-to-understand comparison. Update these ratings periodically as more efficient models become available.<\/p>\n<p><strong>Save Detailed Logs.<\/strong> Keep comprehensive logs of all benchmarking data. For instance, CodeCarbon saves data to a CSV file (<code>emissions.csv<\/code>), and you can integrate outputs with monitoring platforms like <a href=\"https:\/\/prometheus.io\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Prometheus<\/a> for deeper analysis. These metrics help compare models directly and guide efforts to improve energy efficiency.<\/p>\n<h6 id=\"sbb-itb-58f115e\" tabindex=\"-1\" style=\"display: none;color:transparent;\">sbb-itb-58f115e<\/h6>\n<h2 id=\"understanding-and-comparing-benchmark-results\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Understanding and Comparing Benchmark Results<\/h2>\n<p>After gathering your energy benchmarking data, the real challenge begins: interpreting those numbers to guide your AI model selection. Raw energy consumption figures alone aren\u2019t enough &#8211; they need context. By understanding how different metrics interact, you can uncover the trade-offs between performance and efficiency, leading to smarter model comparisons and better choices.<\/p>\n<h3 id=\"understanding-benchmark-metrics\" tabindex=\"-1\">Understanding Benchmark Metrics<\/h3>\n<p>Energy benchmarking provides a range of metrics that are essential for decision-making. One of the most basic yet crucial measurements is <strong>energy per inference<\/strong>, typically expressed in microjoules (\u03bcJ) for smaller models or watt-hours for larger systems. For instance, <a href=\"https:\/\/mlcommons.org\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">MLPerf<\/a> Tiny measures energy consumption in \u03bcJ per inference, making it ideal for evaluating resource-constrained setups like IoT devices.<\/p>\n<p>For larger language models, the <strong>AI Energy Score<\/strong> uses GPU energy consumption, measured in <strong>watt-hours per 1,000 queries<\/strong>, as its main metric. This standardized measurement ensures fair comparisons across models, regardless of size or architecture. Additionally, the AI Energy Score employs a <strong>1-to-5 star rating system<\/strong>, where 5 stars indicate the most energy-efficient models and 1 star the least efficient.<\/p>\n<p><strong>Composite metrics<\/strong> go a step further by capturing the balance between efficiency and speed. One such metric is the <strong>Energy Delay Product (EDP)<\/strong>, calculated by multiplying energy consumption by execution time (EDP = E \u00d7 T). This metric is particularly valuable for devices where both efficiency and speed are critical, such as battery-powered systems.<\/p>\n<blockquote>\n<p>&quot;The EDP is a widely recognized metric in the literature and is commonly used in latency-sensitive applications to quantify efficiency.&quot; \u2013 Pietro Bartoli et al., Politecnico di Milano <\/p>\n<\/blockquote>\n<p>Take, for example, tests conducted in May 2025 on the <a href=\"https:\/\/www.st.com\/en\/microcontrollers-microprocessors\/stm32n6-series.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">STM32N6 MCU<\/a>. Switching from High to Low Performance reduced the DSCNN model&#8217;s energy consumption from 219.0 \u00b1 4.1 \u03bcJ to 156.5 \u00b1 3.9 \u03bcJ. This adjustment resulted in a <strong>27% improvement in EDP<\/strong>, with only a minor increase in latency. Such insights highlight how composite metrics can uncover optimization opportunities that single metrics might miss.<\/p>\n<p>The <strong>relative Energy Delay Product (rEDP)<\/strong> further simplifies this concept by showing EDP percentage changes compared to a reference configuration. This makes it easier to communicate efficiency improvements to stakeholders. Of course, energy efficiency alone isn\u2019t enough &#8211; a model must still perform its intended tasks effectively. Metrics like <strong>accuracy, throughput, and latency<\/strong> remain critical. For this reason, the AI Energy Score initiative requires models to meet predefined accuracy thresholds before they can earn energy efficiency ratings.<\/p>\n<p>Once you understand these metrics, you can use them in comparison tables to make well-informed model selections.<\/p>\n<h3 id=\"using-comparison-tables-for-model-selection\" tabindex=\"-1\">Using Comparison Tables for Model Selection<\/h3>\n<p>Comparison tables are a practical way to evaluate models by combining energy metrics with performance indicators. These tables should include both direct energy measurements and relative efficiency ratings alongside key performance metrics like accuracy, precision, recall, and F1-scores.<\/p>\n<p>For instance, a May 2025 evaluation of climate data showed that the <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/phi\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Phi-4<\/a> model (14.7B parameters) achieved an accuracy of 0.8 &#8211; just 7% lower than the top-performing Qwen3-235B-A22B model (235B parameters). However, <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/phi\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Phi-4<\/a> used <strong>24 times less energy<\/strong> (12.69 Wh vs. 286 Wh) to complete the same task. A table summarizing these findings could look like this:<\/p>\n<figure class=\"table\" style=\"width: 100%;max-width: 100%;overflow-x: scroll;\">\n<table>\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Parameters<\/th>\n<th>Accuracy<\/th>\n<th>Energy (Wh)<\/th>\n<th>Relative Efficiency<\/th>\n<th>Performance Trade-off<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Qwen3-235B-A22B<\/td>\n<td>235B<\/td>\n<td>0.867<\/td>\n<td>286<\/td>\n<td>Baseline<\/td>\n<td>&#8211;<\/td>\n<\/tr>\n<tr>\n<td>Phi-4<\/td>\n<td>14.7B<\/td>\n<td>0.8<\/td>\n<td>12.69<\/td>\n<td>24\u00d7 more efficient<\/td>\n<td>7% accuracy reduction<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>Tables like these are even more powerful when they include <strong>before and after optimization<\/strong> data. For example, quantization and local inference techniques can reduce carbon emissions for large language models by up to 45%. A comparison of <a href=\"https:\/\/www.llama.com\/models\/llama-3\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Llama 3.2<\/a>&#8216;s performance before optimization (0.45 accuracy, 0.012 kg CO\u2082) versus after optimization (0.48 accuracy, 0.005 kg CO\u2082) demonstrates how efficiency improvements can also enhance overall performance.<\/p>\n<p>Breaking down energy consumption by stages &#8211; such as preprocessing, prefill, and decoding &#8211; can further refine your analysis. Modern benchmarking tools make this possible, helping you identify where optimizations will have the greatest impact.<\/p>\n<p>Energy efficiency isn\u2019t just about sustainability &#8211; it also directly reduces operational costs, especially in large-scale deployments. This makes it a key consideration for enterprises.<\/p>\n<p>It\u2019s essential to keep these comparison tables up to date. For example, the AI Energy Score leaderboard is recalibrated roughly every six months to reflect technological advancements. What qualifies as a 5-star efficiency rating today might be considered average in the near future.<\/p>\n<p>When selecting models, always test them in environments that closely resemble your actual deployment setup. Factors like hardware configurations, batch sizes, and workload patterns can significantly affect energy consumption. Your tables should account for these real-world variables.<\/p>\n<blockquote>\n<p>&quot;The AI Energy Score builds on existing initiatives like MLPerf, Zeus, and Ecologits by focusing solely on standardized energy efficiency benchmarking for AI inference. Unlike MLPerf, which prioritizes performance with optional energy metrics, or Zeus and Ecologits, which may be limited by open-source constraints or estimation methods, the AI Energy Score provides a unified framework that evaluates both open-source and proprietary models consistently.&quot; \u2013 AI Energy Score <\/p>\n<\/blockquote>\n<p>For procurement processes, consider including energy transparency requirements in your RFPs and tenders. Ask vendors to provide AI Energy Scores or equivalent energy consumption metrics. This not only promotes transparency but also encourages the adoption of energy-efficient AI practices across the industry.<\/p>\n<h2 id=\"optimizing-ai-models-for-better-energy-efficiency\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Optimizing AI Models for Better Energy Efficiency<\/h2>\n<p>Once you&#8217;ve benchmarked your AI models, the next step is to optimize them to lower energy consumption while maintaining performance. Techniques like quantization can shrink model sizes by as much as 75\u201380% with minimal impact on accuracy. This not only reduces operational costs but also helps cut emissions. These strategies build on benchmarking by directly addressing energy demands.<\/p>\n<h3 id=\"selecting-energy-efficient-architectures\" tabindex=\"-1\">Selecting Energy-Efficient Architectures<\/h3>\n<p>The journey to energy-efficient AI begins with choosing the right model architecture. Larger models with more parameters naturally consume more energy, but bigger doesn\u2019t always mean better for every task.<\/p>\n<p>One promising approach is using <strong>sparse models<\/strong>, which focus only on the components needed for a specific task. This can reduce computation requirements by 5 to 10 times. By comparison, dense models process every parameter, regardless of relevance.<\/p>\n<p><strong>Small Language Models (SLMs)<\/strong> are another efficient choice, especially for tasks in resource-constrained environments like edge devices. These models deliver robust performance for targeted tasks while consuming far less power than their larger, general-purpose counterparts.<\/p>\n<p><strong>Mixture of Experts (MoE)<\/strong> architectures take efficiency a step further. These models consist of multiple specialized sub-models, but they only activate the ones relevant to a given task. This selective activation minimizes computational load and energy use while retaining the advantages of specialization.<\/p>\n<p>Matching your model to the task at hand is also critical. For example, using a massive model like <a href=\"https:\/\/en.wikipedia.org\/wiki\/GPT-4\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">GPT-4<\/a> (with over 1 trillion parameters) for a simple text classification task is overkill. A smaller, specialized model with just a few million parameters could achieve similar accuracy while using significantly less energy.<\/p>\n<h3 id=\"fine-tuning-and-prompt-engineering\" tabindex=\"-1\">Fine-Tuning and Prompt Engineering<\/h3>\n<p>After selecting an efficient architecture, you can further lower energy consumption through techniques that simplify models and streamline their operation.<\/p>\n<ul>\n<li><strong>Quantization<\/strong> reduces model size by using lower-precision numbers, which decreases memory and computation demands with minimal accuracy loss.<\/li>\n<li><strong>Pruning<\/strong> removes unnecessary parameters and connections from overparameterized models. When done carefully, this can significantly reduce size and energy use without hurting performance.<\/li>\n<li><strong>Knowledge distillation<\/strong> trains smaller &quot;student&quot; models to mimic larger &quot;teacher&quot; models. These student models are much smaller but retain most of the teacher&#8217;s performance. For instance, DistilBERT delivers 97% of BERT\u2019s performance while being 40% smaller and 60% faster.<\/li>\n<\/ul>\n<p>For maximum results, these methods can be combined. For example, pruning followed by quantization can create models that are 4\u20135 times smaller and 2\u20133 times faster.<\/p>\n<p>Even <strong>well-crafted prompts<\/strong> can improve efficiency by reducing the number of tokens processed. Tools like <a href=\"https:\/\/godofprompt.ai\" style=\"display: inline;\">God of Prompt<\/a> offer guides and optimized prompts to help streamline operations across various AI platforms.<\/p>\n<figure class=\"table\" style=\"width: 100%;max-width: 100%;overflow-x: scroll;\">\n<table>\n<thead>\n<tr>\n<th>Optimization Technique<\/th>\n<th>Energy Benefits<\/th>\n<th>Accuracy Impact<\/th>\n<th>Best Use Cases<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Quantization<\/td>\n<td>75\u201380% size reduction<\/td>\n<td>&lt;2% accuracy loss<\/td>\n<td>Large-scale inference, edge devices<\/td>\n<\/tr>\n<tr>\n<td>Pruning<\/td>\n<td>30\u201350% parameter reduction<\/td>\n<td>Minimal with tuning<\/td>\n<td>Overparameterized models<\/td>\n<\/tr>\n<tr>\n<td>Knowledge Distillation<\/td>\n<td>90\u201395% teacher performance retained<\/td>\n<td>Significant size reduction<\/td>\n<td>Resource-limited environments<\/td>\n<\/tr>\n<tr>\n<td>Combined (Quantization + Pruning)<\/td>\n<td>4\u20135\u00d7 smaller, 2\u20133\u00d7 faster<\/td>\n<td>Varies by implementation<\/td>\n<td>Production deployments<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<h3 id=\"regular-monitoring-and-updates\" tabindex=\"-1\">Regular Monitoring and Updates<\/h3>\n<p>Optimization doesn\u2019t stop once a model is deployed. Continuous monitoring is essential to sustain energy efficiency throughout the AI lifecycle.<\/p>\n<blockquote>\n<p>&quot;Continuous monitoring of energy consumption during the operation of AI systems is essential for sustainable deployment. Utilizing tools that provide real-time energy consumption data can help teams make informed decisions on possible optimizations and adjustment needs. This proactive monitoring aids in maintaining the efficiency of AI applications throughout their life cycle.&quot;<\/p>\n<ul>\n<li>OrhanErgun.net Blog <\/li>\n<\/ul>\n<\/blockquote>\n<p>Tools like CodeCarbon track energy usage for general machine learning models, whether running locally or in the cloud. For large language models, platforms like MLCarbon provide detailed tracking across their entire lifecycle.<\/p>\n<p>This data isn\u2019t just for show &#8211; it should guide real improvements. For example, you can adjust model complexity dynamically based on workload and energy data. Some organizations even use carbon-aware AI systems that optimize tasks based on the carbon intensity of the power source.<\/p>\n<p>Regular updates to software and hardware are also key. These updates often include performance improvements that reduce energy use. Staying current with optimization techniques ensures your models remain as efficient as possible.<\/p>\n<p>Google&#8217;s &quot;4Ms&quot; framework (Model, Machine, Mechanization, and Map) highlights how systematic optimization can slash energy use by up to 100\u00d7 and CO\u2082 emissions by up to 1,000\u00d7 during machine learning training.<\/p>\n<blockquote>\n<p>&quot;Energy management is an ongoing process. Businesses should continuously monitor consumption data and adapt their strategies as new patterns emerge, ensuring long-term energy efficiency and cost savings.&quot;<\/p>\n<ul>\n<li>Simon Stano and J. Mark Munoz, California Management Review <\/li>\n<\/ul>\n<\/blockquote>\n<p>For high-volume systems, daily monitoring and monthly reviews are recommended. Smaller deployments may need less frequent attention. Keep in mind that inference often accounts for the bulk of AI\u2019s energy consumption due to its repetitive use across millions &#8211; or even billions &#8211; of users. Regular updates and optimizations are essential to keep energy usage in check.<\/p>\n<h2 id=\"conclusion\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Conclusion<\/h2>\n<p>Benchmarking AI models plays a critical role in ensuring efficient performance while keeping costs and energy use in check. Consider this: global data centers consume about 1\u20131.3% of the world\u2019s electricity, with energy use growing by 20\u201340% annually. AI applications alone contribute to 10\u201320% of that consumption. These numbers highlight the importance of monitoring and optimizing energy usage in AI systems.<\/p>\n<p>By systematically measuring, comparing, and refining AI models, benchmarking provides actionable insights for improvement. Earlier sections covered tools and techniques for tracking energy consumption and outlined strategies &#8211; like pruning or quantization &#8211; that can cut energy use by as much as 50%.<\/p>\n<p>A major energy drain in AI comes from inference, which can account for over 80% of a model\u2019s total lifecycle energy use. This makes optimization efforts especially impactful, as they reduce energy consumption every time the model is used. For instance, a single GPT-4o query consumes 0.42 Wh &#8211; about 40% more energy than a typical Google search at 0.30 Wh. When scaled to millions of users, even small efficiency improvements can lead to significant energy savings.<\/p>\n<p>The industry is already moving toward greater energy transparency. Benchmarking initiatives are updated regularly to drive progress, and energy efficiency is becoming a key factor in procurement decisions. As Dr. Sasha Luccioni of <a href=\"https:\/\/huggingface.co\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">Hugging Face<\/a> explains:<\/p>\n<blockquote>\n<p>&quot;The AI Energy Score represents a major milestone for sustainable AI. By creating a transparent rating system, we address a key blocker for reducing the environmental impact of AI. We&#8217;re thrilled to launch this project and look forward to seeing wider adoption.&quot; <\/p>\n<\/blockquote>\n<p>To reduce both costs and environmental impact, measure your models\u2019 energy use and apply optimization techniques. Whether it\u2019s pruning unnecessary parameters, adopting quantization, or transitioning to more efficient architectures like sparse models, every step counts.<\/p>\n<p>Continuous monitoring is equally important. Automate tracking systems, set internal benchmarks for sustainability, and make energy efficiency a priority when selecting models. As highlighted earlier, these practices &#8211; when combined with regular updates and fine-tuning &#8211; can substantially lower energy costs and carbon footprints.<\/p>\n<blockquote>\n<p>&quot;For organizations using AI\/ML technologies, it is crucial to systematically track the carbon footprint of the ML lifecycle and implement best practices in model development and deployment stages.&quot; \u2013 Lakshmithejaswi Narasannagari, Senior Developer, InfoQ <\/p>\n<\/blockquote>\n<p>Striking a balance between performance and sustainability is essential for the future of AI. By embedding energy benchmarking into your workflow now, you\u2019re not just optimizing models &#8211; you\u2019re laying the groundwork for responsible AI that scales without depleting our planet\u2019s resources.<\/p>\n<h2 id=\"faqs\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">FAQs<\/h2>\n<h3 id=\"how-can-businesses-optimize-ai-model-performance-while-minimizing-energy-use\" tabindex=\"-1\" data-faq-q>How can businesses optimize AI model performance while minimizing energy use?<\/h3>\n<p>Businesses can strike a balance between AI performance and energy efficiency by using <strong>optimization techniques designed to save energy<\/strong>. These approaches help cut down on computational demands while still maintaining accuracy. On top of that, AI itself can play a role in streamlining data center operations &#8211; making resource allocation smarter and scheduling workloads more efficiently.<\/p>\n<p>AI-driven tools can also make a big difference in areas like cooling systems and energy grid management. By adopting smarter infrastructure and refining operational strategies, businesses can boost performance while cutting down on energy use, reducing their overall environmental footprint.<\/p>\n<h3 id=\"what-tools-and-methods-can-you-use-to-measure-the-energy-efficiency-of-ai-models\" tabindex=\"-1\" data-faq-q>What tools and methods can you use to measure the energy efficiency of AI models?<\/h3>\n<p>To evaluate how energy-efficient AI models are, you can rely on specialized tools and frameworks built for this purpose. One example is the <strong>AI Energy Score<\/strong>, which provides a standardized way to assess and compare energy consumption across models. Similarly, <strong>MLPerf Power<\/strong> focuses on system-level energy efficiency by monitoring power usage during AI tasks. These tools often feature automated benchmarking, consistent metrics, and public leaderboards to ensure reliable and transparent evaluations.<\/p>\n<p>Using these resources can help you better understand the energy demands of your AI models. They also reveal opportunities for improvement, allowing you to refine your models for better performance while reducing their environmental impact.<\/p>\n<h3 id=\"why-should-ai-models-be-monitored-and-updated-regularly-even-after-improving-their-energy-efficiency\" tabindex=\"-1\" data-faq-q>Why should AI models be monitored and updated regularly, even after improving their energy efficiency?<\/h3>\n<p>Regular monitoring and timely updates are crucial to keep AI models performing at their best and ready to handle new challenges. Even after fine-tuning energy efficiency, unexpected shifts in data patterns, system needs, or operating conditions can arise, potentially affecting how well the models function.<\/p>\n<p>By keeping a close eye on performance, you can catch anomalies early, avoid potential breakdowns, and ensure the model stays dependable. Regular updates not only enhance safety but also help cut operational costs and maintain stability &#8211; especially important in dynamic and intricate energy systems.<\/p>\n<h2>Related Blog Posts<\/h2>\n<ul>\n<li><a href=\"\/blog\/ultimate-guide-to-real-time-ai-roi-tracking\" style=\"display: inline;\">Ultimate Guide to Real-Time AI ROI Tracking<\/a><\/li>\n<li><a href=\"\/blog\/understanding-the-real-cost-of-ai-agents\" style=\"display: inline;\">Understanding the Real Cost of AI Agents<\/a><\/li>\n<li><a href=\"\/blog\/ai-model-selection-balancing-cost-and-quality\" style=\"display: inline;\">AI Model Selection: Balancing Cost and Quality<\/a><\/li>\n<li><a href=\"\/blog\/frameworks-for-gpt-benchmarking-guide\" style=\"display: inline;\">Frameworks for GPT Benchmarking: Guide<\/a><\/li>\n<\/ul>\n<p><script async type=\"text\/javascript\" src=\"https:\/\/app.seobotai.com\/banner\/banner.js?id=68dd7438e3dd4bddfa63c4d6\"><\/script><script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"How can businesses optimize AI model performance while minimizing energy use?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<\/p>\n<p>Businesses can strike a balance between AI performance and energy efficiency by using <strong>optimization techniques designed to save energy<\/strong>. These approaches help cut down on computational demands while still maintaining accuracy. On top of that, AI itself can play a role in streamlining data center operations - making resource allocation smarter and scheduling workloads more efficiently.<\/p>\n<p>AI-driven tools can also make a big difference in areas like cooling systems and energy grid management. By adopting smarter infrastructure and refining operational strategies, businesses can boost performance while cutting down on energy use, reducing their overall environmental footprint.<\/p>\n<p>\"}},{\"@type\":\"Question\",\"name\":\"What tools and methods can you use to measure the energy efficiency of AI models?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<\/p>\n<p>To evaluate how energy-efficient AI models are, you can rely on specialized tools and frameworks built for this purpose. One example is the <strong>AI Energy Score<\/strong>, which provides a standardized way to assess and compare energy consumption across models. Similarly, <strong>MLPerf Power<\/strong> focuses on system-level energy efficiency by monitoring power usage during AI tasks. These tools often feature automated benchmarking, consistent metrics, and public leaderboards to ensure reliable and transparent evaluations.<\/p>\n<p>Using these resources can help you better understand the energy demands of your AI models. They also reveal opportunities for improvement, allowing you to refine your models for better performance while reducing their environmental impact.<\/p>\n<p>\"}},{\"@type\":\"Question\",\"name\":\"Why should AI models be monitored and updated regularly, even after improving their energy efficiency?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<\/p>\n<p>Regular monitoring and timely updates are crucial to keep AI models performing at their best and ready to handle new challenges. Even after fine-tuning energy efficiency, unexpected shifts in data patterns, system needs, or operating conditions can arise, potentially affecting how well the models function.<\/p>\n<p>By keeping a close eye on performance, you can catch anomalies early, avoid potential breakdowns, and ensure the model stays dependable. Regular updates not only enhance safety but also help cut operational costs and maintain stability - especially important in dynamic and intricate energy systems.<\/p>\n<p>\"}}]}<\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to effectively benchmark AI models for energy efficiency, optimizing performance while reducing costs and environmental impact.<\/p>\n","protected":false},"author":1,"featured_media":3990,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[],"class_list":["post-3991","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-at-work"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to Benchmark AI Models for Energy Efficiency | God of Prompt<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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We publish prompts, guides, and playbooks for ChatGPT, Claude, Gemini, and Midjourney.\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/godofprompt.ai\\\/blog\\\/#\\\/schema\\\/person\\\/d50f21f5201cf68185421f5fd87ed94f\",\"name\":\"Robert Youssef\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/d48b5a1e20bcb1d5a09591608fd744bc4303937062c5cbd00961fe65302db773?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/d48b5a1e20bcb1d5a09591608fd744bc4303937062c5cbd00961fe65302db773?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/d48b5a1e20bcb1d5a09591608fd744bc4303937062c5cbd00961fe65302db773?s=96&d=mm&r=g\",\"caption\":\"Robert Youssef\"},\"description\":\"The Missing Link I come from architecture and urban planning, designing systems that should have created leverage&mdash;transit networks, resource flows, development infrastructure. This work taught me how things should scale. When I shifted to helping businesses automate and implement AI, I kept seeing the same gap everywhere. Businesses had the technology. They had the need. But they were missing the layer in between&mdash;the infrastructure for how to actually communicate with AI. Developers spoke in functions. Clients spoke in outcomes. AI spoke in&hellip; whatever you prompted it to speak in. Nobody had a shared language. No protocols. No architecture. The Infrastructure Layer With generative AI becoming so essential, I stopped seeing AI as a tool and started seeing it as territory that needed architecture. People were treating it like a magic search bar. Ask once, get disappointed, move on. They were standing in front of a transit system but couldn&rsquo;t read the map. I realized: They don&rsquo;t need better AI. They need better infrastructure between them and AI. Prompts aren&rsquo;t requests&mdash;they&rsquo;re protocols. Communication architecture. The same thinking I used mapping resource flows in cities applied perfectly to designing how humans should interact with intelligence. Building the System @godofprompt became that infrastructure layer. Not a course. Not a tool. An intelligent system for how information should flow between human thinking and AI capability. Same principles that prevented scope creep in urban development now prevent prompt failures. Same patterns that identified bottlenecks in city budgets now identify bottlenecks in AI workflows. Turns out you don&rsquo;t need a bigger budget or better AI. You need someone who knows how to design the space between question and answer. 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We publish prompts, guides, and playbooks for ChatGPT, Claude, Gemini, and Midjourney."},{"@type":"Person","@id":"https:\/\/godofprompt.ai\/blog\/#\/schema\/person\/d50f21f5201cf68185421f5fd87ed94f","name":"Robert Youssef","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/d48b5a1e20bcb1d5a09591608fd744bc4303937062c5cbd00961fe65302db773?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/d48b5a1e20bcb1d5a09591608fd744bc4303937062c5cbd00961fe65302db773?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/d48b5a1e20bcb1d5a09591608fd744bc4303937062c5cbd00961fe65302db773?s=96&d=mm&r=g","caption":"Robert Youssef"},"description":"The Missing Link I come from architecture and urban planning, designing systems that should have created leverage&mdash;transit networks, resource flows, development infrastructure. This work taught me how things should scale. When I shifted to helping businesses automate and implement AI, I kept seeing the same gap everywhere. Businesses had the technology. They had the need. But they were missing the layer in between&mdash;the infrastructure for how to actually communicate with AI. Developers spoke in functions. Clients spoke in outcomes. AI spoke in&hellip; whatever you prompted it to speak in. Nobody had a shared language. No protocols. No architecture. The Infrastructure Layer With generative AI becoming so essential, I stopped seeing AI as a tool and started seeing it as territory that needed architecture. People were treating it like a magic search bar. Ask once, get disappointed, move on. They were standing in front of a transit system but couldn&rsquo;t read the map. I realized: They don&rsquo;t need better AI. They need better infrastructure between them and AI. Prompts aren&rsquo;t requests&mdash;they&rsquo;re protocols. Communication architecture. The same thinking I used mapping resource flows in cities applied perfectly to designing how humans should interact with intelligence. Building the System @godofprompt became that infrastructure layer. Not a course. Not a tool. An intelligent system for how information should flow between human thinking and AI capability. Same principles that prevented scope creep in urban development now prevent prompt failures. Same patterns that identified bottlenecks in city budgets now identify bottlenecks in AI workflows. Turns out you don&rsquo;t need a bigger budget or better AI. You need someone who knows how to design the space between question and answer. That&rsquo;s AI architecture for me.","sameAs":["https:\/\/www.linkedin.com\/in\/rryssf\/","https:\/\/x.com\/https:\/\/x.com\/rryssf"],"url":"https:\/\/godofprompt.ai\/blog\/author\/robert-youssef\/"}]}},"_links":{"self":[{"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/posts\/3991","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/comments?post=3991"}],"version-history":[{"count":0,"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/posts\/3991\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/media\/3990"}],"wp:attachment":[{"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/media?parent=3991"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/categories?post=3991"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/godofprompt.ai\/blog\/wp-json\/wp\/v2\/tags?post=3991"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}