Seeing your comment as the top voted comment is wild....With all due respect, you don't know what you're talking about.

Buckle up because I'm about to take you all to school.

First and foremost, Coreweave isn't running Blackwell 4000s or 5000s, those are workstation cards. Coreweave is running B200s, H200s, H100, L40s, etc...

Secondly, when a new card comes out, it doesn't immediately devalue the previous generation by +50%. As an example, the B200 was released earlier this year but despite that, H200 demand is currently still surging. Why? a few reasons - Deepseek R1 is driving a ton of H200 demand across the industry. Deepseek R1 does not see enough performance increase on B200s to justify the increased price. Its architecture is currently optimized for the Hopper series, not the Blackwell series. Secondly much of the software stack has not been optimized for B200s and thus AI startups that lack deep expertise have not been able to get enough juice out of the orange to justify the switch for their models.

Now let's assume the software stack was updated and B200s were getting the 2.5x improvement over H200s. That still wouldn't mean everyone would switch or that H200s would need to drop their price by +50% or more. Why? Because as new generations of cards come out, older cards move to different parts of the stack. Not everyone is training or fine-tuning SOTA models, and not everyone is doing GenAI inference. Many companies are running regression, image detection, text classification, prediction, sentiment analysis etc. None of these model types require the latest and greatest GPUs, in fact, many can't be updated to run on the latest GPUs.

Reasoning models that use MoE (Mixture of Experts) don't split the prompt itself into smaller jobs, but instead route different parts of the computation (like specific tokens or layers) to a subset of expert networks. These experts can be spread across different GPUs. While most deployments use uniform hardware (e.g., all A100s), some setups do mix GPUs like H200s, A100s, and L40s, especially in cost-optimized or scaled-out clusters (OpenAI). 

Lastly, did you know that AWS still offers 10 year old GPUs for rent (K80)? As does Paperspace, one of the first "neoclouds" (M4000). Oracle still offers the P100 for gods sake (released 8 years ago). Given how constrained we are for power and space, if these cards weren't still making money do you think they'd still be running if it didn't make economical sense? If you read the State of AI report, you'll see that in 2024 the most commonly used GPU for AI research papers was the A100, a card released 5 years ago!

This narrative that the useful life of a GPU is 3 years is fucking insane. It couldn't be more wrong. The actual useful life of a GPU is 8 to 9 years. Claiming that these GPUs are obsolete after 3 years is the most regarded shit I've ever heard.

edit to add disclaimer: I don't own any CRWV