Key Takeaways: Agentic AI, reasoning models, mixture-of-expert architectures, and reservoir computing are changing how AI workloads use compute resources. Agentic AI is particularly popular, and because it requires more CPU, CPU supply is coming under pressure. Mixture-of-experts models are becoming very popular, enabling specialized queries that require fewer parameters to load. Convolutional neural networks (CNNs) and large language models (LLMs) have dominated commercial AI for some time. Proprietary recommender models for companies such as Meta have also had significant societal impact even though only one company employs each such model. GPUs perform the heavy lifting for many of those types of models. But agentic AI needs more orchestration, and that’s a job for CPUs. When AI’s only job is to run a model, it turns to GPUs or dedicated neural processing units (NPUs). Agents still use these models, but each is only part of the solution. Those agents must make decisions based on what the models return, and that requires if-then-else logic, and that’s what CPUs are good at. GPUs, not so much. According to some industry estimates, agentic workloads could alter the ratio of CPUs to GPUs from 1:4 to as low as 1:1. Newer model types are also emerging, most of which are still based on LLM fundamentals. Unlike agentic AI, reasoning models, mixture-of-expert (MoE) models, and reservoir computing still rely on GPUs. But they’re also ripe for use by agents. Pixels and parsing For several years, practical, commercial AI meant CNNs and recurrent neural networks, or RNNs. The former were good at analyzing camera input, while the latter were proficient at analyzing sound and language. The second “L” in LLM means “language,” but LLMs do much more than language today. The notion is that some piece of text is abstracted into a token, and now practically anything can be a token. The benefit of LLMs over RNNs is that they can maintain an arbitrarily long context, making them better at picking the right words when interpreting and generating text. In addition to requiring fewer resources, RNNs can analyze incoming frames of sound immediately without waiting to establish context, improving real-time response. The challenge with early LLMs was that they had one job — to predict a likely next word in a sequence. That word had to make sense syntactically, but not necessarily semantically, and it certainly didn’t check facts to ensure correctness, hence the frequent “hallucinations.” One way to improve that was through retrieval-augmented generation (RAG). Instead of just figuring out a word that could maybe work, models could access “embeddings” that contained real facts, helping generate text that was both syntactically, semantically, and factually correct. Secret agent man Today’s hot topic is, of course, agentic AI. An agent is a new entity, not one that replaces or modifies an LLM (or any other model). It effectively represents a higher level of abstraction, acting as an orchestrator of models. It’s not a model itself, although it must be trained. “There is some new research out there that says agentic inference is going to dwarf everything else put together before it,” said Ed Doe, CEO and co-founder of Delos Data. The whole point of an agent is that it does more than find an answer to a question. It does something with that answer. What it does will likely depend on the answers it’s received so far. In other words, it can send tokens to a model and, when it receives the result, decide what to do next, embarking on a sequence of decisions and actions. A well-trained agent is meant to handle a set of tasks without tight supervision. “If, as a leader, you have to tell your team every step to accomplish, you’re going to be more consumed with every step of the way,” explained Rob Knoth, product management director, digital and signoff group at Cadence . “But if you say, ‘I need you to go over there and get this work done,’ then you can go focus o