4 ms·
Mythic's analog compute-in-memory architecture
- alex7o 1mo agoThis looks cool a chiplet can fit 30m params so the biggest card can fit qwen 3.8 27b it would be cool to see some benchmarks on things like that publically.
- phdelightful 1mo agoMy understanding (perhaps outdated) is that manufacturing variability is a key challenge for analog computing.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-million-funding-round/ https://www.eetimes.com/mythic-rises-from-the-ashes-with-125...
- trebligdivad 1mo agoI'd assume it does some type of calibrate per device (regularly?) or the design is such that it's differential so things cancel out. Note it's also on 28nm for the analogue bits because yes it's harder.
- speps 1mo agoYou reminded me the anecdote about every SID chip sounding different. If you hear a recording of a C64 made song, it’s unique to that chip (and somewhat to the machine as well, timing, crystal, etc.).
- chrisjj 1mo agoIts a myth.
- yencabulator 1mo agoDifferent generations of chips, or various cheap clone chips, sure. Within one generation, evidence of significant differences seems weaker. Sure, manufacturing had loose tolerances, you could end up with a "faulty" chip, but most SIDs sounded the same. You could also end up with a 6510 that crashed after 30 minutes. https://www.youtube.com/watch?v=yadWu7hs4MU https://www.youtube.com/watch?v=yadWu7hs4MU https://www.youtube.com/watch?v=n73-ipWMCCY https://www.youtube.com/watch?v=n73-ipWMCCY
- sroussey 1mo agoIf you go for a full curve over the space, then yes. But like NAND, there is room for, I don’t know what to call it, “quantization”? You get a few values out instead of just binary. My guess is, like other issues of precision, errors can get out of hand if you are not careful. But being an EE in another life, I can tell you the power waste and slowness of ALUs is kinda wild. I definitely believe that Gaming on CPUs is like ML on GPUs. Slow, and waiting for something more appropriate to come along. Don’t know if these guys are the ones to do it (and they are, ahem, not alone). But someone will deliver 100x to 1000x boost either in speed, power efficiency, or both.
- chrisjj 1mo agoSince no sane person expects reliable results from these chatbots, unreliable analog implementations should be fine.
- deleted 1mo ago[deleted]
- zerd 1mo agoVeritasium did a video on this 4 years ago. https://www.youtube.com/watch?v=GVsUOuSjvcg https://www.youtube.com/watch?v=GVsUOuSjvcg
- MichaelNolan 1mo agoIf 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 1mo agoIf they can't demonstrate it publicly it's probably fake.
- mdp2021 1mo agoThe tech for that is planned for release next year.
- dgfl 1mo 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 1mo 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?
- tancop 1mo agoTheir 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.
- vatsachak 1mo agoLike the numbers they claim could literally make LLMs 20x profitable. If it were true then why isn't every AI company trying to buy them out?
- refulgentis 1mo agoc.f. https://news.ycombinator.com/item?id=49403836 https://news.ycombinator.com/item?id=49403836, then from there, you'd need to see a couple of orders of magnitude before it's tractable for LLMs. Bottom of page linked from HN (currently https://www.mythic.ai/ https://www.mythic.ai/) indicates they're hoping to demonstrate something that could that in 2028 or later, and both Nvidia and Cerebra are looking at 10x'ing models to 10T+ plus in 2027. So they may never catch up on LLMs. They're a good fit for the companies they're working with and have taken investment from, ex. Toyota, that aren't doing LLMs.
- vatsachak 1mo agoThey plan to be able to run 1T parameter models next year https://www.mythic.ai/vanguard https://www.mythic.ai/vanguard Seems big, IF true
- mdp2021 1mo agoNotice that they do not talk about SRAM when they present the M1 chiplets - but you have to store the kv-cache etc. somewhere to run LLMs. The technology that could run LLMs should be the "Vanguard", but as the homepage says, "the M1 (scope: Edge/Cameras/Drones) is there, the Vanguard should be a reality in 2027".
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- mdp2021 1mo ago> 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.
- sroussey 1mo agoI think we are at the apex of Von Neumann machines. Once there is enough money to make alternatives, they will thrive. And AI is that catalyst.
- imtringued 1mo ago[dead]
- dgfl 1mo agoThe vanguard chiplets even steps down to 30M… but they do claim to have a “Mead” design [1] designed to run GPT-3 in a single chip. 3D NAND flash can indeed routinely store hundreds of GB per die, so that’s proven. The question is about all the peripheral circuitry needed. Each attention block would need its own KV cache (i.e. SRAM or DRAM somewhere), plus DAC/ADC inputs and outputs, unless they figure out a way to keep it analog all the way (really cool but unlikely). I think this field is very interesting, at least from a technology point of view. Whether it works out or not will sadly be a matter of economics more than physics I fear. [1] https://www.mythic.ai/mead https://www.mythic.ai/mead
- mdp2021 1mo ago> unless they figure out a way to keep it analog all the way (really cool but unlikely) Very unlikely for the connection to the cache RAM, seemingly impossible if we want to get an articulate output :) > they do claim to have a “Mead” design [1] designed to run GPT-3 [ - i.e. to hold a 175b NN - ] in a single chip Careful: that is the /intention/, but the chip is just the storage (and CiM) for the NN and other parts are missing - explained in the Vanguard, not explained in the Mead. > Whether it works out or not will sadly be a matter of economics more than physics I fear Yes, but: -- what is not enabled today may often be tomorrow through advances, esp. in the economy of production; -- a very great point about this product seems to be that the chips can be rewritten, the NN is not etched and static (cpr. Taalas): that makes the practical implications extremely relevant, the demand would be "screaming mob" like; -- we have to go in that direction (of NNs in CiM) anyway, so it's just a matter of time, effort after effort we will get there.
- api 1mo agoSo much work is being done on running these things more efficiently, and it’s why I think the data center build out is a huge bubble.
- amluto 1mo agoI’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.
- dgfl 1mo agoThis page seems pretty in depth: https://www.mythic.ai/supply-chainmanufacturing https://www.mythic.ai/supply-chainmanufacturing They are using GlobalFoundries’ 28nm node for the floating gate transistors, afaiu, which they then bond onto a TSCM 5nm digital I/O wafer. Analog computation’s principles are sound. It’s mostly doing matrix vector multiplications though. The rest is digital.