this is really interesting as I was not expecting a 65 billion parameter model to be performing as well as models over 10x its size on agentic coding tasks. but the intuition comes in when we think about how much of the parameters are just stored knowledge and memory, as opposed to this cognitive core that Karpathy described a little while back. in this case, it has less ability to store knowledge, and we can think of it as 65 billion active parameters for cognitive functioning. not to mention that this is a great recipe for super fast pre-training because they don't have to worry about the complexities of balancing and stabilizing an MoE. this bad boy can fit on a single RTX Pro 6000 Blackwell at NVFP4 precision with the DFlash drafter getting probably really good performance for local setups. I bet there will be some interesting post-trains done on this model which will be extremely good at certain types of agentic coding tasks given that they said they had a light amount of post-training claude code snapshot attached