Updates since then: * Deepseek v4 is out. There *is* a 2-bit quant that can run within 90 GB ( https://huggingface.co/antirez/deepseek-v4-gguf ), and it works, however it's only fast on Apple hardware (I've head ~35 tok/s). On AMD, it's ~7 tok/s. IMO actually taking the effort to properly support more than one hardware manufacturer is a great example of the difference between mere "decentralized AI" and genuine "CROPS AI". I hope we can become better at this. * https://github.com/vbuterin/messaging-daemon also has alpha telegram support now. However, the path to adding your account is quite janky * https://github.com/Luce-Org/lucebox-hub looks promising as a way to run "dense" models (eg. Qwen 27B) more efficiently. It's janky, but on my 5090 laptop it seems to be ~2x more tok/s than llama.cpp * VoxTerm (local AI recording, no third-party servers) continues to be developed https://github.com/dmarzzz/VoxTerm And there's a lot more projects coming on the horizon. One other thing that has been on my mind is that there's actually a lot of intersection between "CROPS ethereum access layer" and "CROPS AI". For example, we want a ZK way to make (paid) calls to remote LLMs. But if we have this, then it's just as useful for solving another problem: private RPC reads in Ethereum. Another example: application-specific finetuned LLMs. Leanstral ( https://mistral.ai/news/leanstral ; I get ~38 tok/s on AMD) fits into < 70 GB, but can hold its own against 1T models on writing Lean code. Things like this are a huge boon for writing more secure code ( https://vitalik.eth.limo/general/2026/05/18/fv.html ). We should have models finetuned for Ethereum-related use cases as well.(vitalik.eth)