AMD Surpasses 30x25 Goal, Sets Ambitious New 20x Efficiency Target

1. Includes high-performance CPU and GPU accelerators used for AI training and High-Performance Computing in a 4-Accelerator, CPU hosted configuration. Goal calculations are based on performance scores as measured by standard performance metrics (HPC: Linpack DGEMM kernel FLOPS with 4k matrix size; AI training: lower precision training-focused floating-point math GEMM kernels operating on 4k matrices) divided by the rated power consumption of a representative accelerated compute node including the CPU host + memory, and 4 GPU accelerators.
2. AMD based advanced racks for AI training/inference in each year (2024 to 2030) based on AMD roadmaps, also examining historical trends to inform rack design choices and technology improvements to align projected goals and historical trends. The 2024 rack is based on the MI300X node, which is comparable to the Nvidia H100 and reflects current common practice in AI deployments in 2024/2025 timeframe. The 2030 rack is based on an AMD system and silicon design expectations for that time frame. In each case, AMD specified components like GPUs, CPUs, DRAM, storage, cooling, and communications, tracking component and defined rack characteristics for power and performance. Calculations do not include power used for cooling air or water supply outside the racks but do include power for fans and pumps internal to the racks.Performance improvements are estimated based on progress in compute output (delivered, sustained, not peak FLOPS), memory (HBM) bandwidth, and network (scale-up) bandwidth, expressed as indices and weighted by the following factors for training and inference.

   	FLOPS	HBM BW	Scale-up BW
   Training	70.0%	10.0%	20.0%
   Inference	45.0%	32.5%	22.5%

   Performance and power use per rack together imply trends in performance per watt over time for training and inference, then indices for progress in training and inference are weighted 50:50 to get the final estimate of AMD projected progress by 2030 (20x).  The performance number assumes continued AI model progress in exploiting lower precision math formats for both training and inference which results in both an increase in effective FLOPS and a reduction in required bandwidth per FLOP.  
3. https://www.amd.com/en/newsroom/press-releases/2020-6-25-amd-exceeds-six-year-goal-to-deliver-unprecedented.html
4. EPYC-030a: Calculation includes 1) base case kWhr use projections in 2025 conducted with Koomey Analytics based on available research and data that includes segment specific projected 2025 deployment volumes and data center power utilization effectiveness (PUE) including GPU HPC and machine learning (ML) installations and 2) AMD CPU and GPU node power consumptions incorporating segment-specific utilization (active vs. idle) percentages and multiplied by PUE to determine actual total energy use for calculation of the performance per Watt. 38x is calculated using the following formula: (base case HPC node kWhr use projection in 2025 * AMD 2025 perf/Watt improvement using DGEMM and TEC +Base case ML node kWhr use projection in 2025 *AMD 2025 perf/Watt improvement using ML math and TEC) /(Base case projected kWhr usage in 2025). For more information, https://www.amd.com/en/corporate/corporate-responsibility/data-center-sustainability.html.
5. AMD estimated the number of racks to train a typical notable AI model based on EPOCH AI data (https://epoch.ai). For this calculation we assume, based on these data, that a typical model takes 10²⁵ floating point operations to train (based on the median of 2025 data), and that this training takes place over 1 month.  FLOPs needed = 10^25 FLOPs/(seconds/month)/Model FLOPs utilization (MFU) = 10^25/(2.6298*10^6)/0.6.   Racks = FLOPs needed/(FLOPS/rack in 2024 and 2030). The compute performance estimates from the AMD roadmap suggests that approximately 276 racks would be needed in 2025 to train a typical model over one month using the MI300X product (assuming 22.656 PFLOPS/rack with 60% MFU) and <1 fully utilized rack would be needed to train the same model in 2030 using a rack configuration based on an AMD roadmap projection. These calculations imply a >276-fold reduction in the number of racks to train the same model over this six-year period. Electricity use for a MI300X system to completely train a defined 2025 AI model using a 2024 rack is calculated at ~7GWh, whereas the future 2030 AMD system could train the same model using ~350 MWh, a 95% reduction. AMD then applied carbon intensities per kWh from the International Energy Agency World Energy Outlook 2024 [https://www.iea.org/reports/world-energy-outlook-2024]. IEA’s stated policy case gives carbon intensities for 2023 and 2030. We determined the average annual change in intensity from 2023 to 2030 and applied that to the 2023 intensity to get 2024 intensity (434 CO2 g/kWh) versus the 2030 intensity (312 CO2 g/kWh). Emissions for the 2024 baseline scenario of 7 GWh x 434 CO2 g/kWh equates to approximately 3000 metric tC02, versus the future 2030 scenario of 350 MWh x 312 CO₂ g/kWh equates to around 100 metric tCO2. 
6. Regression analysis of achieved accuracy/parameter across a selection of model benchmarks, such as MMLU, HellaSwag, and ARC Challenge, show that improving efficiency of ML model architectures through novel algorithmic techniques, such as Mixture of Experts and State Space Models for example, can improve their efficiency by roughly 5x during the goal period. Similar numbers are quoted in Patterson, D., J. Gonzalez, U. Hölzle, Q. Le, C. Liang, L. M. Munguia, D. Rothchild, D. R. So, M. Texier, and J. Dean. 2022. "The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink." Computer. vol. 55, no. 7. pp. 18-28.”
   Therefore, assuming innovation continues at the current pace, a 20x hardware and system design goal amplified by a 5x software and algorithm advancements can lead to a 100x total gain by 2030.

This blog contains forward-looking statements concerning Advanced Micro Devices, Inc. (AMD) and AMD’s new 20x rack-scale energy efficiency target for AI systems by 2030 including AMD’s ability to achieve the new target, projected benefits and next steps, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are commonly identified by words such as "would," "may," "expects," "believes," "plans," "intends," "projects" and other terms with similar meaning. Investors are cautioned that the forward-looking statements in this blog are based on current beliefs, assumptions and expectations, speak only as of the date of this blog and involve risks and uncertainties that could cause actual results to differ materially from current expectations. Such statements are subject to certain known and unknown risks and uncertainties, many of which are difficult to predict and generally beyond AMD's control, that could cause actual results and other future events to differ materially from those expressed in, or implied or projected by, the forward-looking information and statements. Material factors that could cause actual results to differ materially from current expectations include, without limitation, the following: Intel Corporation’s dominance of the microprocessor market and its aggressive business practices; Nvidia’s dominance in the graphics processing unit market and its aggressive business practices; competitive markets in which AMD’s products are sold; the cyclical nature of the semiconductor industry; market conditions of the industries in which AMD products are sold; AMD’s ability to introduce products on a timely basis with expected features and performance levels; loss of a significant customer; economic and market uncertainty; quarterly and seasonal sales patterns; AMD's ability to adequately protect its technology or other intellectual property; unfavorable currency exchange rate fluctuations; ability of third party manufacturers to manufacture AMD's products on a timely basis in sufficient quantities and using competitive technologies; availability of essential equipment, materials, substrates or manufacturing processes; ability to achieve expected manufacturing yields for AMD’s products; AMD's ability to generate revenue from its semi-custom SoC products; potential security vulnerabilities; potential security incidents including IT outages, data loss, data breaches and cyberattacks; uncertainties involving the ordering and shipment of AMD’s products; AMD’s reliance on third-party intellectual property to design and introduce new products; AMD's reliance on third-party companies for design, manufacture and supply of motherboards, software, memory and other computer platform components; AMD's reliance on Microsoft and other software vendors' support to design and develop software to run on AMD’s products; AMD’s reliance on third-party distributors and add-in-board partners; impact of modification or interruption of AMD’s internal business processes and information systems; compatibility of AMD’s products with some or all industry-standard software and hardware; costs related to defective products; efficiency of AMD's supply chain; AMD's ability to rely on third party supply-chain logistics functions; AMD’s ability to effectively control sales of its products on the gray market; long-term impact of climate change on AMD’s business; impact of government actions and regulations such as export regulations, tariffs and trade protection measures, and licensing requirements; AMD’s ability to realize its deferred tax assets; potential tax liabilities; current and future claims and litigation; impact of environmental laws, conflict minerals related provisions and other laws or regulations; evolving  expectations from governments, investors, customers and other stakeholders regarding corporate responsibility matters; issues related to the responsible use of AI; restrictions imposed by agreements governing AMD’s notes, the guarantees of Xilinx’s notes, the revolving credit agreement and the ZT Systems credit agreement; impact of acquisitions, joint ventures and/or strategic investments on AMD’s business and AMD’s ability to integrate acquired businesses, including ZT Systems; AMD’s ability to sell the ZT Systems manufacturing business; impact of any impairment of the combined company’s assets; political, legal and economic risks and natural disasters; future impairments of technology license purchases; AMD’s ability to attract and retain qualified personnel; and AMD’s stock price volatility. Investors are urged to review in detail the risks and uncertainties in AMD’s Securities and Exchange Commission filings, including but not limited to AMD’s most recent reports on Forms 10-K and 10-Q.