Nvidia's $20 Billion Bet on Groq Signals the Inference Hardware Revolution
The Inference Hardware Revolution of 2026
AI's focus has shifted from training to inference, driven by reasoning models and agentic AI that run continuously. This surge has led to unexpected alliances: OpenAI and Amazon deploy Cerebras chips, Nvidia acquired Groq's talent and IP for $20 billion, and Anthropic pays SpaceXAI over $1 billion monthly for compute. Startups like d-Matrix and Majestic Labs are challenging Nvidia's GPU dominance with memory-centric designs that stack compute on DRAM or extend memory interfaces, aiming to overcome the memory bottleneck that leaves GPUs idle 50-80% of the time.
With the GPU-based approach, you end up greatly over-provisioning compute and starved on memory. That's driving the big [memory] scale out.
- aschla
"If AI inference remains as desirable as Kimball expects, the evolution is likely to follow the same trajectory as the CPU. The CPU didn’t improve along a single axis but instead across simultaneously. Once transistor scaling slowed, chip and system architecture innovations of all kinds proliferated. The list of individual innovations that led to today’s ubiquitous, powerful personal compute could fill dozens of books. A few decades from now, the history of AI inference innovation will show similar depth."
Of the areas mentioned in the article, which are the most likely to have the most prominent innovative impact, and what will they entail?
- ninju
Great read.
I like how the author uses the analogy of scrabble word creation to describe LLM training but unfortunately the analogy didn't continue to inference and I got lost trying to keep up.
- _superposition_
Excellent article. I believe the majority of benchmark performance gains moving forward will come from this side of the stack enabling faster iteration/recursion.