Mythic's analog compute-in-memory chip delivers 100x energy efficiency for AI
Mythic's analog compute-in-memory architecture
Mythic's analog compute-in-memory architecture stores AI model weights in flash memory and computes directly at the source, eliminating the energy waste of shuttling data between processor and memory. The APU (Analog Processing Unit) achieves 100x greater efficiency than GPUs, validated by Honda and the U.S. Department of Defense. Products include the M1 for edge applications and the upcoming M2 chiplet platform for enterprise LLMs. With the acquisition of Videantis, Mythic's ADAS platform is already in 30 million vehicles.
We store AI model weights inside flash memory and compute in analog, directly at the source, eliminating the waste at its origin.
- phdelightful
My understanding (perhaps outdated) is that manufacturing variability is a key challenge for analog computing. Digital designs are also fundamentally analogue under the hood, but if you only need to resolve a 0 or 1 you are much more tolerant of any source of noise. I wouldn't mind hearing even a little bit more from Mythic about how they make this work in practice.
A 2026 EE Times article [1] refers to "compensation" and "calibration" techniques.
[1] https://www.eetimes.com/mythic-rises-from-the-ashes-with-125...
- amluto
I’m willing to believe that one could design a little circuit that multiplies a number stored in a floating-gate MOSFET by an analog input and another circuit that adds the result to an accumulator (in fact there seems to be some prior art from 1989!). But I don’t know who would fab this - I doubt this is something doable is TSMC’s standard process.
And maybe one can use NAND or NOR flash with a different sort of controller to do analog computation, and maybe one could convince a flash memory fab to build it for you.
But there is no mention on the site of how they expect to manufacture the thing.
- MichaelNolan
If you’re looking for their LLM page it’s 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.
- mdp2021
> Mythic M1 stores up to 80 million neural network weight parameters directly on-chip
Which means connecting ~350 chiplets to run a Qwen 3.8 27b and over 30000 chiplets to run Qwen3.8-2.4T-A95B. Cost? Space? Feasibility?
Edit: wrong values, lost a zero...
Edit: seemingly, the M1 is only part of the whole need. With the M1, you would run a feedforward pass of the NN but use the rest of the Von Neumann architecture to manage the data. The pass in the M1 will be lightning fast, the rest still a bottleneck. The M1 is almost explicitly not for LLMs.
- tancop
Their numbers look too good to be true, they have no identified customers and the whole site is generated, but I think the principle behind it is good. If they can pull off the error correction needed to make analog reliable we might have a great new option for cheaper more eco friendly AI. Then again it could turn out to be a total scam.