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Since the Rome Series, AMD has been able to take more market share with the Milan Series and Bergamo Series with improvements such as 3D stacking in Zen3, tripling the L3 cache size while only adding four clock cycles of latency, and further customizing CPUs for cloud native workloads with less cache and more performance per watt. Genoa was the 4th generation, and provided more cache for general purpose workloads.

Training is the process of a model learning patterns from labeled data through internal parameters (called weights). There is forward and backward pass or propagation for updating the parameters. This phase is computationally intensive, requiring significant memory and parallel processing power.

Training is where Nvidia’s strengths are nearly insurmountable as the leader in combining parallel processing (CUDA) cores with matrix computations (Tensor Cores). Over the past few years, Nvidia has increased compute power by an order of magnitude to the point of defying Moore’s Law with architectural changes such as tensor cores and lower precision floating points.

Inference takes batches of real-world data and quickly comes back with an answer or prediction --- therefore, this stage needs low latency (or speed) over raw compute power. For example, inference will take a trained model and produce a probable match for new data in milliseconds. While it can be compute-intensive for large models like GPT-4, inference generally prioritizes low latency, higher efficiency, and lower cost.

In many applications, it makes sense to run inference at the edge (closer to where data is generated). However, cloud inference is still widely used for models that are too large or resource-demanding to deploy on local devices. Compared to training, inference requires only the forward pass through the model, making it more efficient in terms of power and hardware demands.

If we go back and look at how AMD was able to take on Intel -- briefly, it was with an architecture that required less power at nearly half the cost. This helps illustrate that AMD’s strengths are a much better fit for inference rather than training.

Meta, OpenAI, xAI, Oracle, Microsoft, Cohere, HUMAIN, Red Hat, Astera Labs and Marvell discussed how they are partnering with AMD for AI solutions

The latest version of the AMD open-source AI software stack, ROCm 7, is engineered to meet the growing demands of generative AI and high-performance computing workloads—while dramatically improving developer experience across the board. ROCm 7 features improved support for industry-standard frameworks, expanded hardware compatibility and new development tools, drivers, APIs and libraries to accelerate AI development and deployment.

AMD also announced the broad availability of the AMD Developer Cloud for the global developer and open-source communities.

SANTA CLARA, Calif., March 31, 2025 (GLOBE NEWSWIRE) -- AMD (NASDAQ: AMD) today announced the completion of its acquisition of ZT Systems, a leading provider of AI and general-purpose compute infrastructure for the world’s largest hyperscale providers. The acquisition will enable a new class of end-to-end AI solutions based on the combination of AMD CPU, GPU and networking silicon, open-source AMD ROCm™ software and rack-scale systems capabilities. It will also accelerate the design and deployment of AMD-powered AI infrastructure at scale optimized for the cloud.

AMD expects the transaction to be accretive on a non-GAAP basis by the end of 2025. The world-class design teams will join the AMD Data Center Solutions business unit led by AMD Executive Vice President Forrest Norrod. AMD is actively engaged with multiple potential strategic partners to acquire ZT Systems’ industry-leading U.S.-based data center infrastructure manufacturing business in 2025.

“With the rapid pace of innovation in AI, reducing the end-to-end design and deployment time of cluster-level data center AI systems will be a significant competitive advantage for our customers,” said Forrest Norrod, executive vice president and general manager, Data Center Solutions business unit at AMD.

Former ZT Systems Founder and CEO Frank Zhang joins AMD as senior vice president of ZT Manufacturing, reporting to Forrest Norrod, where he will help lead the divestiture of the manufacturing business. Former ZT Systems President Doug Huang joins AMD as senior vice president of Data Center Platform Engineering, also reporting to Forrest Norrod. In this role, he will lead design and customer enablement teams, working closely with the AMD Data Center Solutions business unit and AI Group to accelerate time-to-market for data center AI solutions.

SANTA CLARA, Calif., Aug. 12, 2024 (GLOBE NEWSWIRE) -- AMD (NASDAQ: AMD) today announced the completion of its acquisition of Silo AI, the largest private AI lab in Europe. The all-cash transaction valued at approximately $665 million furthers the company’s commitment to deliver end-to-end AI solutions based on open standards and in strong partnership with the global AI ecosystem.

Silo AI brings a team of world-class AI scientists and engineers to AMD experienced in developing cutting-edge AI models, platforms and solutions for large enterprise customers including Allianz, Philips, Rolls-Royce and Unilever. Their expertise spans diverse markets and they have created state-of-the-art open source multilingual Large Language Models (LLMs) including Poro and Viking on AMD platforms. The Silo AI team will join the AMD Artificial Intelligence Group (AIG), led by AMD Senior Vice President Vamsi Boppana.

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