The Rubin family of chips is sold as a complete "system as a rack," meaning customers can't just swap out old GPUs. This technical requirement creates a forced, expensive upgrade cycle for cloud providers, compelling them to invest heavily in entirely new rack systems to stay competitive.

Despite the rapid pace of hardware innovation, the value of older NVIDIA GPUs like the H100 is holding strong. Cloud provider CoreWeave reports these chips are retaining 90-95% of their pricing power over a 5-6 year lifespan because compute demand far outstrips supply.

Despite the rollout of Blackwell and the announcement of Vera Rubin, cloud provider Nebius reports that pricing for older Hopper GPUs is not dropping, and in some cases is rising. This shows a persistent market for "good enough" compute for specific workloads.

Contrary to typical hardware depreciation, GPUs like NVIDIA's H100 are becoming more valuable over time. This is because newer, more efficient AI models can generate significantly more output and value on the same hardware, tying the GPU's worth to its utility rather than its age.

Contrary to the belief that AI hardware becomes obsolete quickly, older GPUs like A100s will have a long depreciable life. As companies optimize costs, they'll use model routing to send simple queries to older, cheaper hardware, extending its utility for six to eight years.

Contrary to the assumption that customers only want the latest chips, Nvidia's older H200s are still being heavily purchased. This is because they fit the power profile of older data centers that cannot support the massive energy draw of newer systems, making them a more practical and immediately profitable choice for many operators.

Countering the narrative of rapid burnout, CoreWeave cites historical data showing a nearly 10-year service life for older NVIDIA GPUs (K80) in major clouds. Older chips remain valuable for less intensive tasks, creating a tiered system where new chips handle frontier models and older ones serve established workloads.

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.

The rental prices for older NVIDIA GPUs, like the Hopper family and A100s, are increasing. This counterintuitive trend shows demand for AI compute is so far outstripping total supply that even previous-generation hardware is becoming more valuable, highlighting the severity of the GPU crunch.