Technology

The relentless pursuit of faster artificial intelligence is often framed as an exponential climb, but the reality is more akin to building a pyramid. From a distance, the structure appears smooth and continuously rising. Up close, however, it’s a series of jagged blocks, each representing a solved bottleneck. The current race to deliver real-time AI, where models can “think” and respond with human-like speed, is hitting a new limestone block, and the players positioning to overcome it are Nvidia and, increasingly, Groq.

For decades, the industry has relied on Moore’s Law – the observation, initially made by Intel co-founder Gordon Moore, that the number of transistors on a microchip doubles approximately every two years – as a guiding principle. Moore, who passed away in March 2023, initially predicted a doubling every year, later revised to 18 months. While that law has slowed in the realm of CPUs, the growth in computing power shifted to GPUs, and now, the focus is shifting again, demanding new architectural approaches.

The current wave of AI is powered by transformer architecture, but simply scaling up brute force compute isn’t enough. As Anthropic’s President and co-founder Dario Amodei noted, “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.” The challenge now isn’t just processing data, but enabling AI to reason, self-correct, and iterate – a process that requires dramatically faster inference speeds.

The Latency Problem and Groq’s Approach

The biggest gains in AI reasoning capabilities in 2025 are being driven by improvements in “inference time compute” – the speed at which a model can generate responses. But speed is paramount; users won’t wait minutes for an AI to formulate an answer. Here’s where Groq enters the picture with its focus on lightning-rapid inference. By combining architectural efficiency, like the Mixture of Experts (MoE) technique, with Groq’s throughput, the potential for “frontier intelligence” becomes attainable.

Nvidia recently highlighted the MoE technique in its Rubin press release, noting it can accelerate AI reasoning and inference at up to 10x lower cost per token. This underscores the understanding that achieving exponential growth requires more than just raw processing power; it demands architectural innovation. Groq’s Language Processing Unit (LPU) architecture addresses a key bottleneck in GPUs – memory bandwidth – during minor-batch inference, delivering significantly faster results.

From Universal Chip to Inference Optimization

For the past decade, GPUs have been the workhorse for all things AI, handling both training and inference. However, as AI models evolve towards “System 2” thinking – where they reason and self-correct – the computational demands shift. Training requires massive parallel processing, while inference, particularly for reasoning tasks, demands faster sequential processing to generate tokens instantly.

Consider the expectations for AI agents: autonomous flight booking, code generation, and legal research. These tasks require models to generate numerous internal “thought tokens” to verify their work before presenting a final answer. On a traditional GPU, 10,000 thought tokens might take 20 to 40 seconds, leading to user frustration. Groq, however, can accomplish the same task in under 2 seconds.

Nvidia and Groq: A Potential Convergence

If Nvidia were to integrate Groq’s technology, it could solve the “waiting for the robot to think” problem, preserving the promise of AI. Just as Nvidia transitioned from rendering pixels for gaming to rendering intelligence for generative AI, it could now move towards rendering reasoning in real-time. This integration would too create a significant software moat, leveraging Nvidia’s CUDA ecosystem to complement Groq’s hardware.

Combining this raw inference power with next-generation open-source models, like the rumored DeepSeek 4, could yield an offering that rivals today’s leading models in cost, performance, and speed. This opens up opportunities for Nvidia to expand its cloud offerings and continue powering the growing demand for AI solutions.

Returning to the pyramid analogy, AI growth isn’t a smooth line of FLOPs; it’s a staircase of bottlenecks being overcome. The GPU solved the initial calculation bottleneck, transformer architecture addressed the depth of learning, and now, Groq’s LPU is tackling the challenge of “thinking” fast enough. Jensen Huang’s willingness to embrace disruptive technologies positions Nvidia to continue leading the charge.

By validating Groq, Nvidia isn’t simply acquiring a faster chip; it’s investing in the future of intelligence. The convergence of architectural innovation and raw processing power will be critical for enterprises looking to unlock the full potential of AI. The race to real-time AI is on, and the companies that can deliver on this promise will be the ones that win.

The next phase will likely focus on optimizing the software stack to fully leverage these hardware advancements and exploring new model architectures that further enhance reasoning capabilities. Continued innovation in both hardware and software will be essential to push the boundaries of what’s possible with AI.

What are your thoughts on the future of AI inference? Share your insights in the comments below.