Mythic's analog compute-in-memory architecture | Hacker News Reader

Mythic's analog compute-in-memory architecture

mythic.ai

65 pointsjanandonly35 comments

My understanding (perhaps outdated) is that manufacturing variability is a key challenge for analog computing.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...

If 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.

You 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.).

Different 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=n73-ipWMCCY

Its a myth.

Veritasium did a video on this 4 years ago. https://www.youtube.com/watch?v=GVsUOuSjvcg

I'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.

Since no sane person expects reliable results from these chatbots, unreliable analog implementations should be fine.

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.

Joke’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

But 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 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?

The tech for that is planned for release next year.

If they can't demonstrate it publicly it's probably fake.

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.

This page seems pretty in depth: 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.

> 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.

The 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

> 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.

This might end up being a strong use case for FeFET FRAM, nonvolatile, high density, and sram fast. In an alternative universe where fram infra was as widespread as flash, memory is fast, cheap, and nonvolatile. It’s just that flash built the infrastructure and climbing that ladder is nearly impossible.

I wouldn't really say SRAM-fast. Maybe DRAM-fast? At least in write speed. But the thing is, you don't need high write speed for this application. And read speed should be the same between flash and FeFETs.

Btw, I've seen some roadmaps in which foundries were heading towards integrating ferroelectrics and floating gates to accelerate switching. The two technologies are not mutually exclusive really, so I think we might see something in the relatively near future. The crazy memory market may help!

I 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.

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.

> Their numbers look too good to be true

Why? I have not seen anything outlandish for a NN implementation (vs a NN simulation).

> can pull off the error correction

There lie the issues that have not been mentioned, the solutions not explained. Analog computing means: * costly digital-to-analog at the input and analog-to-digital at the output; * sensitivity to environmental conditions such as temperature; * signal dispersion hence the need to boost it in the path.

Maybe checking the patents they registered?

Temperature is a relatively trivial issue because it can either be compensated or the chip simply kept at a constant temperature (they’re low power chips anyway, throttling could be skipped to simplify the design). The ADCs and DACs are indeed the main thing though. The whole analog compute game is about making those power efficient and scaled enough that the math still works out in your favor. The demos do work though, this company is far from the only one; see [1] or [2] for example.

Flash NAND can routinely be bought with 4 bits per cell, perhaps even 5 soon (QLC and PLC drives). Since it has been proven to store 4 digital bits at production scale, I’m willing to bet an analog architecture running an LLM should be able to yield the performance analog of a 4-8 bit quantized model. Where in that 4-8 range is pretty crucial, but it depends on the specific design.

[1] https://www.nature.com/articles/s41928-023-01010-1 [2] https://www.nature.com/articles/s41586-022-04992-8

(Moderation, the post https://news.ycombinator.com/item?id=49404099 from SkyPuncher is legit. Automated filter? Maybe you can give us the ability to vouch?)

My 15 second read of just the front page aligned with you, but when I saw replies pushing back, I went back and loaded News, then cross-verified some of the claims. It's real.

Like 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?

Notice 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".

c.f. 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/) 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.

They plan to be able to run 1T parameter models next year

https://www.mythic.ai/vanguard

Seems big, IF true

I'm pretty sure the answer is that their chip is uniquely unsuited for LLMs, probably because the write speed might be horrendously slow. Most likely too slow for storing the context window in multi user workloads.

Bulk bitwise operations in a memory array driven by an out-of-spec controller is a thing, but I'm not sure why they're using NAND for this and not DRAM like everyone else, and why it's so small (just 80M weights). And the page is insufferable and doesn't tell anything about how it works. Everything about it seems off.

I went down this rabbit hole earlier in the year.

You generally don’t need error correction. Instead, you do a retraining of sorts where you basically fine tune the model on the target chip architecture. Because these systems are so complex, errors tend to localize without affecting overall output.

My understanding is the hardest part is actually noise in the analog system.

This 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.

So 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.

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