From CPUs to GPUs and Beyond: The Shifting Paradigms of AI Compute

The world of compute has always been a tale of sprints and plateaus, with each new technology wave bringing its own set of challenges and opportunities. Gordon Moore's famous law-that the number of transistors on a microchip doubles every year-held true for decades, making Intel’s CPUs the poster child of exponential growth. However, as CPU performance began to plateau in recent years, the baton was passed to GPUs, with Nvidia leading the charge.

The Illusion of Smooth Growth

While it's easy to get caught up in the narrative of exponential growth, the reality is far more nuanced. Technology advancements are often marked by significant leaps followed by periods of stabilization. In the context of AI, the current wave is driven by transformer architectures, which have been pushing the boundaries of what’s possible. Dario Amodei, President and co-founder of Anthropic, aptly captures this sentiment: “The exponential continues until it doesn’t. And every year we’ve been like, ‘Well, this can’t possibly be the case that things will continue on the exponential’-and then every year it has.”

However, just as CPUs gave way to GPUs, we are witnessing another shift in the landscape of AI compute. Late in 2024, DeepSeek surprised the industry by training a world-class model on a surprisingly small budget, leveraging the Mixture of Experts (MoE) technique. This method allows for more efficient and scalable model training, which is crucial as models continue to grow in size and complexity.

The Latency Crisis: Where Groq Fits In

The biggest gains in AI reasoning capabilities in 2025 were driven by "inference time compute"-essentially, allowing the model to think for a longer period. However, this comes at a cost. Time is money, and both consumers and businesses are increasingly intolerant of delays.

This is where Groq enters the picture with its lightning-fast inference capabilities. Groq’s architecture is designed to handle massive-scale models with unprecedented speed, addressing the latency crisis that has been a significant bottleneck in AI deployment.

Technical Details

• MoE Technique: The Mixture of Experts (MoE) technique allows different parts of a model to specialize in different tasks, reducing redundancy and improving efficiency. This is particularly useful for large-scale models where not all parts of the network need to be active at the same time.

• Nvidia NVLink Interconnect Technology: Nvidia’s latest generations of NVLink technology are designed to accelerate agentic AI, advanced reasoning, and massive-scale MoE model inference. According to a recent press release, this technology can achieve up to 10x lower cost per token.

• Groq’s Architecture: Groq’s approach is centered around high-speed inference, which is critical for real-time applications. Their hardware is optimized to handle the computational demands of large models without compromising on speed or efficiency.

Why It Matters

For enterprises, the shift from CPUs to GPUs and now to specialized AI architectures like those offered by Groq represents a significant opportunity. By reducing latency and improving inference times, these technologies enable more efficient deployment of AI models, leading to better user experiences and cost savings.

As we continue to push the boundaries of what’s possible with AI, it’s clear that the next limestone block in the pyramid of compute is already being laid. The future belongs to those who can adapt and innovate, leveraging the latest advancements to stay ahead of the curve.