NVIDIA is launching powerful CPUs like the RTX Spark not just to compete with Apple, but because the primary AI workload is shifting. While GPUs dominate AI training, powerful CPUs are becoming essential for running agentic tools and inference, marking a resurgence for the CPU in the AI hardware landscape.

Meta's multi-billion dollar deal to rent Amazon's Graviton 5 CPUs, not just GPUs, signals a potential architectural shift for AI. This move suggests that CPU architecture could be more efficient or cost-effective for agentic workloads, challenging the conventional wisdom that GPUs are the only viable hardware for scaling AI applications.

The focus on GPUs for AI overlooks a critical bottleneck: CPU shortages. AI agents require massive CPU power for non-GPU tasks like web queries and data prep. This demand is straining existing infrastructure and creating new market opportunities for CPU makers like ARM.

While GPUs are key for model training, the next AI wave of autonomous agents relies more on CPUs. The task of controlling and orchestrating multiple agents and tool calls is fundamentally a CPU-based process. This is creating a new hardware bottleneck and shifting focus to CPU manufacturers.

The current AI boom focuses on GPUs for "thinking" (Gen AI). The next phase, "Agentic AI" for "doing," will rely heavily on CPUs for task orchestration and memory for context, creating new investment opportunities in this previously overshadowed hardware.

AMD's success isn't just about stealing market share from competitors. The rise of 'agentic inference' in AI is massively expanding the total addressable market for data center CPUs. This creates a "share-grabbing" scenario where new demand provides greenfield growth opportunities for all major players.

The AI hardware market isn't just about NVIDIA. It's a battle between NVIDIA's full-stack system, Google's powerful TPU, and a combined effort where Broadcom builds the networking fabric and custom ASICs, with AMD serving as a plug-in alternative chip.

The demand for AI processing power so vastly outstrips supply that it creates a "compute deficit." This forces major AI players to adopt any viable chip solution they can find, including from AMD. It's not about being better than NVIDIA; it's about being available, ensuring a market for second and third-tier suppliers.

Mark, CTO of AMD, states that the explosion of agentic AI workflows has created an unforeseen demand for a balanced compute architecture. These complex, multi-step processes require a CPU to GPU ratio approaching 1:1, a significant shift from traditional GPU-heavy AI training and inference models.