My understanding (perhaps outdated) is that manufacturing variability is a key challenge for analog computing.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.
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.).
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.
> Mythic M1 stores up to 80 million neural network weight parameters directly on-chip
Which means connecting over 30 chiplets to run a Qwen 3.8 27b and over 3000 chiplets to run Qwen3.8-2.4T-A95B. Cost? Space? Feasibility?
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.
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.
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.
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.
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".
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.
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.
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.
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.).
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.
> Mythic M1 stores up to 80 million neural network weight parameters directly on-chip
Which means connecting over 30 chiplets to run a Qwen 3.8 27b and over 3000 chiplets to run Qwen3.8-2.4T-A95B. Cost? Space? Feasibility?
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.
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.
The tech for that is planned for release next year.
If they can't demonstrate it publicly it's probably fake.
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.
(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?)
> 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?
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
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.
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.