My understanding (perhaps outdated) is that manufacturing variability is a key challenge for analog computing.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.
speps 1 hours ago [-]
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.).
chrisjj 27 minutes ago [-]
Its a myth.
trebligdivad 2 hours ago [-]
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.
chrisjj 28 minutes ago [-]
Since no sane person expects reliable results from these chatbots, unreliable analog implementations should be fine.
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 37 minutes ago [-]
The tech for that is planned for release next year.
vatsachak 1 hours ago [-]
If they can't demonstrate it publicly it's probably fake.
mdp2021 1 hours ago [-]
> 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.
sroussey 35 minutes ago [-]
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.
imtringued 8 minutes ago [-]
[dead]
tancop 2 hours ago [-]
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.
Maybe checking the patents they registered?
vatsachak 1 hours ago [-]
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?
imtringued 2 minutes ago [-]
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.
mdp2021 48 minutes ago [-]
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.
vatsachak 1 hours ago [-]
They plan to be able to run 1T parameter models next year
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.
SkyPuncher 50 minutes ago [-]
[dead]
alex7o 3 hours ago [-]
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.
api 4 minutes ago [-]
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.
Rendered at 22:26:48 GMT+0000 (Coordinated Universal Time) with Vercel.
A 2026 EE Times article [1] refers to "compensation" and "calibration" techniques.
[1] https://www.eetimes.com/mythic-rises-from-the-ashes-with-125...
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.
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.
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.
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?
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.
https://www.mythic.ai/vanguard
Seems big, IF true