6 ms·
If you’re looking for their LLM page it’s https://www.mythic.ai/enterprise-llm https://www.mythic.ai/enterprise-llm I wish they would have done what Taalas did
by MichaelNolan 26d ago
If you’re looking for their LLM page it’s https://www.mythic.ai/enterprise-llm https://www.mythic.ai/enterprise-llm
I wish they would have done what Taalas did with chatjimmy.ai and just directly host a model for us to view, rather than just claiming it’s 50x faster than Nvidia/groq. Their claim is specifically for a 1 trillion param model. So they could have just grabbed GLM 5.2, or similar, and hosted it.
- vatsachak 26d agoIf they can't demonstrate it publicly it's probably fake.
- mdp2021 26d agoThe tech for that is planned for release next year.
- dgfl 25d agoJoke’s on us, all of their pages are LLM pages! LLM generated, that is. Btw, can guarantee that they are not ready to demonstrate that yet. They’re using 2D FLASH with 30M weights per die [1], so to get to 1T they will need… 33,333 dies. Interesting scaling problem to say the least [1] https://www.mythic.ai/vanguard https://www.mythic.ai/vanguard
- mdp2021 25d agoBut they also declare having a "Mead" technology that stores at least 175b NNs in a single chip through 3D stacking - see https://www.mythic.ai/mead https://www.mythic.ai/mead and other posts in this page. A confusing thing is that the goal is tackled through a number of proposals... Why Vanguard if they have Mead? If Mead, how to get the memory integration that are explicit on Vanguard?