Really dumb question from a software guy. Why aren't the labs burning their frontier models into chips already? Seems like the performance gains and cost per request would be worth it. That said, I understand neither the economics nor the physical challenges to doing this.
Model SOTA moves faster than chips can be designed or produced. You'd need to commit to a particular model for years to get payoff while still burning buckets of money producing new SOTA models to keep up with the competition.
It's why everyone and their dog runs these things on GPUs. When a new model supercedes the previous one, so long as you've got the memory for it your chips aren't obsolete.
I'm looking forward to someone picking a model to be "good enough" (say, qwen 4.0 or something) and selling them as peripheral hardware
At this point, LLM's are "good enough" for all kinds of tasks. Instead of making them more capable, now the efforts are making them smaller and cheaper.
IMO this is the dream scenario! Cheaper and faster at the current level of capability gives us incredibly useful tools without the worst of the risks people fear. (Though there are certainly already great risks at the current level of capability as well.)
Ah that makes sense. I'm skeptical that AIs doing things on their own will be competitive with humans using AIs to do things. But I'm not incredibly confident in this and I think it's sensible to think the opposite. But to me, I just think about all the things I can accomplish with the aid of very good, fast, and cheap inference.
I think the sad part is that whether it's any good or not, it will be cheaper, and that will be enough even at current performance levels to cut a huge number of jobs. I think people exist in the present moment largely because legal questions about liability are not settled well enough for executives to start the great purge. I believe on some level my continued employment is simply because people in leadership positions would be incredibly unwise to not have a human to blame when things start going wrong. People are expensive insurance policies for the time being. Once there is some legal framework to absolve executives of their own culpability or they figure out a good way to insulate themselves from personal responsibility, then the real "fun" will begin.
The problem comes in to how many bullshit jobs we'll have to create to ensure that enough people can still earn a living to ensure that we don't have riots and revolution in the street.
Companies being profit seeking entities that are actively hostile to social wellbeing would gladly put all of our money in a machine money making loop and leave all but a few humans out.
Like mostly all tech, AI is destined to get cheaper with time. We're still pretty early on and are experiencing a hardware crunch, which will ease eventually.
AI doesn't have workers rights, doesn't burn out, doesn't get sick, can be instantly onboarded, etc. The second we can be fully replaced with AI, we will be. Plan accordingly. I'm pursuing FIRE and considering moving into a trade.
> The second we can be fully replaced with AI, we will be. Plan accordingly. I'm pursuing FIRE and considering moving into a trade.
Doesn't matter what plans you make, the impact will be across everyone!
Moving into a trade won't help, because the supply is doubling while the demand is lowering. Fewer people with money to spend; they'll fix their own damn toilets if the decision comes down to "buy food" or "hire plumber".
You forget that once all lose their job will all go into trade. Means there will 1000x more people to compete with into trades, which means race to the fucking bottom and lowest salaries if you can even find job. Good for real estate owners, cheap repairs. But probably won’t get any rent paid because people can’t find jobs.
Idk, has china printed houses successfully? And if there are reports of that can we trust that the information hasn’t been gov altered to make a failure seem like a success?
I’m not “leaving them out” i literally just don’t know anything about them.
And yes i am skeptical as a default on this subject. But still - photos don’t actually tell the whole story. What was the cost? How was the project schedule? Etc.
I was skeptical when they first started doing it 20 years ago.
Now, right wrong or indifferent, it’s a known entity there - for a LOT of projects. Enough of them exist now that…I’m skeptical they could find enough stupid people to keep paying for it. I know the government has a hand in a lot, but that only goes so far, for so long…
training data for physical tasks like that is much, much harder than just collecting the entire internet. It'll be resistant to AI much longer than knowledge work will be
We already have construction codes. Fitting techs with AR headsets could gather more data. Existing structures won't be the first step due to variences. The best place to implement this is modular building in controlled automated environments. Even if we enhance the current productivity, we will create a problem due labor supply driving down incomes, especially if people are coming from the other jobs affected by AI.
You can barely capture folding laundry using task data, and you're vastly underestimating how much independent decision making the trades do in their regular work. If I'm honest this whole thing reads like someone who's never been exposed to the actual work.
Out of business, but not necessarily out of existence.
If there is really nothing for humans to do, whoever has control over (not necessarily ownership of) enough robots and AI to directly maintain and grow their collection of robots and AI, has something functionally equivalent to a breeding population of the stuff. (If they can't make more of themselves and humans made the initial batch, then that's a job humans can do so the initial condition has not been reached).
None of this helps people figure out how to look after their own interests in the meantime. The rest of society may carry on as today without AI, akin to the Amish if we're lucky (rejecting further developments unless good for us) or like Pol Pot if we're unlucky (reject anything that nerds like because nerds liked AI and everything breaks down).
On the other hand, the institutions may simply fail to handle reality, like in the Great Depression.
Financial Independence, Retire Early. It's a whole subculture online, based around the idea that if you can grow your investments to ~25X your annual expenses, you can safely stop working and live off those investments.
We've had other "apocalypse" scenarios before (industrial revolution and manufacturing automation) where we successfully transitioned to a new way of living and being productive. The optimist in me says that there's nothing special about AI this time around.
In the current economic system.
There is no natural law that states that only the people lucky enough to be born rich and/or well connected to real the rewards.
Yes this is why I don't invest in hardware. TPU is better than GPU when you have a good model already. The models are already good enough to sell as an accelerator card made as a TPU. I would buy an Opus 5.5 card.
No, there still plenty of tasks that LLMs can’t do well or at all. Yes, we should absolutely make models cheaper but there’s a long way to go until they’re “good enough”. And I’m talking about the frontier closed models too, not just the open ones. So it’s not an “instead of” position we’re in. It’s both.
> Model SOTA moves faster than chips can be designed or produced.
From what I remember working in that area the hardest part is getting masks for a design. Masks were developed in the span of half an year. Masks also reusable, they can be mixed and matched and this is why fabless companies work with fabs to produce specialized masks for them, it saves time for consumer to have masks for some macroblocks prebuilt.
Given some amount of work with the fab before main pipeline set (I think a year long process), one can then spew LM-on-a-chip in six months or less and much more than 2 per year, because there can be several LMs in pipeline.
FPGA speed is a common misconception. They often have run at quite modest frequencies, and come with limited memory capacity compared to a GPU at same price range.
Speed (clocks speed) , is dependent on your design layout, and for any resonably advanced layout, it takes lots of knowledge to push the clockspeed beyond 200Mhz on 'consumer'/prosumer models. (Compare to a few GHz for GPU/CPU).
FPGA speed shine where they can pipeline massively parallel calculations through pipelines with minimal lookups.
Wrong. This is one area where FPGAs have an insanely unfair advantage compared to CPUs and GPUs. Yes the SRAM is limited but you have so many individual blocks and all of them come with dual ports and getting the maximum frequency out of block RAM is much easier than getting the maximum frequency out of programmable logic.
If you wanted the highest possible memory bandwidth while being free to look up hundreds or thousands of independent memory addresses at the same time you're better off with an FPGA.
E.g. with an Efinix Titanium Ti180 you could hypothetically have 2560 simultaneous memory requests per cycle all pointing at a different address and process those requests at 1 Ghz.
Yes you are right, I didnt express what I meant very well.
looking up static values is quick and easy. When you need to lookup results from previous stages of pipeline rather than just feeding them forward, thats where I run into trouble. But I am a relatively fresh FPGA designer, so I am sure it can be done. And I probably need to level up my boards a bit too.
They have trillions worth of obligations to their partners in terms of compute purchase agreements and equity. Burning models to chips isn't really something they can blow money on just for funsies, they need to be able to justify it. The above comments are hypotheses as to why they haven't yet.
It isn't just a matter of speed, it's also a matter of model quality. If they take 6 months to burn Fable to chips, and it takes 2 years to break even between design, custom fab, energy savings, etc, are those chips even worth running when the new models that are running on GPUs at that point are producing 10x better quality results?
Sure, your 2.5 year old models are running faster, but you can't drop prices on them without pushing the break even point further out.
If the cost difference isn't incredibly significant, will people even want to pay for the 2.5 year old model, or will they get more value for their money paying more to get better results from the newer model?
There's a lot of open ended questions that I don't have the insiders knowledge for to suggest whether or not such a capital outlay would be a worthy investment.
My guess is that state of the art stuff will stay on GPUs and models burned into chips will be for "good enough" applications that people are still teasing out. Probably highly specialized models in automated sensor units and such.
FPGAs are FPGAs by virtue of putting on the chips vast, vast arrays of wiring that can be controlled by software. Any given utilization of the FPGA will leave large fractions of the chip resources unused. If you've got a highly stereotypical use case FPGAs will have a "highly stereotypical" set of components being unused, where it would be better to use that space instead to do real work. A lot of people only see the "pro" side of the FPGA proposition without realizing they come with some very substantial "cons" that are intrinsic to the way they work.
I guess that's why they work in particular niche spaces like a synthesizer where you have a max of 8 voices and every voice goes through the same pipeline (osc / filter / env / amp) and everything is necessarily running all the time. In that sense I suppose they're very good for modelling any kind of analog circuitry?
Even then, while there are some amazing FPGA-based synths available, companies like Korg just put their code on a raspberry pi and call it a day. The same is true for emulators (SNES Mini etc. are also just raspberry pis under the hood iirc)
You don't really get an FPGA for capability. CPUs are much more capable, and they're general-purpose so they can do absolutely anything with about the same efficiency and just a little more code.
You get an FPGA for timing. They're less capable, but (in many common design architectures), they output their results once per clock, every clock, on time, every time. If you can hit a fabric clock of say 100MHz, clocking all the weird logic you can stuff in there, it gives 100 million outputs per second, never skipping a single one for any reason (short of total failure). The penalty is that making a small change to your desired "program" can be very expensive, and many things won't be realistically possible at all. Or at least won't fit into a part that you can buy. But things like audio, video, and high-frequency trading love being able to guarantee timing.
(Of course there are other ways to write your FPGA HDL, but that's one of the more common ones. And you do see DDR-style clocking, and similar, every now and then.)
> with about the same efficiency and just a little more code.
It depends. For some things, CPUs don't even come close. An XCVU13P FPGA can handle 1.2Tbps of full-duplex Ethernet @ 1 billion pps. And that part costs less than a grand at moderate qty, and with significantly less power consumption than a CPU that'd be capable of operating a dataplane at these speeds.
The point I was trying to make is that the CPU is a general-purpose creature and doesn't really care what you want it to do. If you had a CPU that could handle 1.2Tbps of Ethernet packets at 1Gpps, it could do a whole lot of other things involving 1.2Tbps of data flow too, very easily, if someone wrote the software. And more. (But you're probably not getting 2.4Tbps out of it, no matter what you do.)
An FPGA can not. There's plenty of things that those XCVU13Ps just can't do, or would do worse than a $1 microcontroller. (Setting aside for a moment implementing a CPU inside the FPGA... which does actually happen in just about every large-enough FPGA design, which is its own discussion....)
I'm not really sure what you're saying here. An FPGA is inherently more general than a CPU because it implements any digital logic including a CPU. They're just more expensive and have clock speed limitations. Saying if you had a CPU that could do 1.2Tbps is just as silly as saying if you had an FPGA that easily operated at 5GHz.
The issue is really the extreme difficulty of programming them, especially for anything sequential (rather than combinatorial). Yes, you can just put down a CPU core, but that's really slow compared to a real CPU and a headache in a lot of ways. (But still is a totally valid and used technique.)
To give a concrete example, suppose you are doing some image processing and you need to write out something like a BMP image file from the pixel data in memory. Suppose, also, that it's dynamic-ish and you can't really precompute the header and just copy it around. This is a horrifying nightmare to do in an FPGA, generally involving writing a custom state machine and populating different bits of the structure on each clock and so on and so on. (This is not an arbitrary example, sadly; I've had to do almost exactly this.) You can do it but it's just plain hard.
On a CPU you're done in five minutes, for a BMP file.
Now imagine you want to write out a JPEG or PNG. On a CPU this is hard enough that you'll probably grab a library... and then still be done in about five minutes. Maybe thirty if something's unusual or you're being extra careful. Maybe a day or two if you decide to write it yourself (don't do this).
On an FPGA? Holy hell you're in pain. You're either going to spend $$$ licensing something or you're going to be there for weeks.
That is why there are jobs that FPGAs don't do. They're not technically impossible, they're infeasibly impossible.
If it is your core business, yes, great, do them. But you'd never decide to stop making wasteful unoptimized BMP files on an FPGA "just because". It's a major chunk of work. But on a CPU, five minutes.
The current largest FPGA, the AMD Versal Premium VP1902 has 18.5 million logic cells. That's not even enough for the smallest whisper.cpp model (75M).
You'd have to order hundreds of thousands of them (or millions) to serve even a single copy of a frontier model, and at that scale inference quickly becomes starved by the speed of light.
Well, you'd use BRAM to store model weights, not fabric. But still, you only get a couple hundred MB for probably close to US $100k per chip.
It's likely that the major FPGA vendors will soon announce parts specifically architected to support LLMs and similar models. But the current generation isn't suitable for that at all.
Would BRAM even have enough bandwidth? The reason I quoted logic cells is because that's the way to get instant throughput, which is practically the only reason to use an FPGA over something like a TPU.
I think so, because memory bandwidth really comes from bus width more than clock speed. You can construct 36-bit wide BRAM arrays with bus width comparable to HBM, just by specifying multiple BRAM arrays in parallel.
But that's not an efficiency an FPGA provides in the first place.
Additionally, it's not clear how well large mask roms scale. For instance the Nintendo switch cartridges were expected to be mask roms, but instead are Macronix's XtraRom technology, which is essentially a flash cell array made denser by removing the erase functionality. So basically the die gets manufactured with all bits at the same state, a late manufacturing step either empties or fills the floating gate of the bits you want different, and then it's treated as pretty close to a mask rom. It's not even clear if the bits can be changed without a bare die, a floating probe array, and specialized hardware. Though, like flash the electrons in the floating gates will eventually tunnel and cause the data to bitrot.
So from that it appears that even at the tens of millions of chips volumes that would make sense for essentially whatever size of maskrom, the memory manufacturers tap out at 128megabit for a mask rom chip, and push you towards something flash esque.
And at the end of the day, flash without the erase functionality is pretty damn close to a mask ROM, and lets you write it near the end of manufacturing rather than at the something close to the metal 1 layer.
8 months ago, Taalas claimed https://taalas.com/the-path-to-ubiquitous-ai/#:~:text=Upcomi... that "Our second model, still based on Taalas’ first-generation silicon platform (HC1), will be a mid-sized reasoning LLM. It is expected in our labs this spring and will be integrated into our inference service shortly thereafter. Following this, a frontier LLM will be fabricated using our second-generation silicon platform (HC2). HC2 offers considerably higher density and even faster execution. Deployment is planned for winter."
Nothing was released in spring, and 2 months ago AMD announced their acquisition of Taalas. That doesn't exactly inspire confidence that their frontier LLM will arrive as promised.
> AMD announced their acquisition of Taalas. That doesn't exactly inspire confidence that their frontier LLM will arrive as promised.
Why not? AMDs chip design experience and production capacity are magnitudes larger than a small startup. Assuming that they acquired Taalas for their technology, I don't see a reason why they couldn't.
I think this company was recently acquired by AMD, so hopefully they'll start getting some this into production. I know OpenAI was working on model-on-a-chip too.
Terrafab is just going to have their entire capacity bought out. Genuinely. Demand will increase to exceed supply no matter how high supply is right now.
Most responses here are along the lines of "model capabilites move too fast to build hardware for".
I think the fact that there are plenty of 1yr+ old models on openrouter serving hundreds of billions of tokens a month shows that there's plenty of use case for models that are "good enough. Cerebras' entire business is serving older models at high speed. I would happily use an opus 4.7 at 15k tokens per second. The intelligence per second of an ASIC still makes sense even with rapidly evolving models.
"Intelligence per second" is a striking phrase! I'll be turning it around my head at a moderate IPS until I hopefully make something of it.
But you're right in sense: moderate intelligence at superhuman rates (and presuming moderate energy usage) is very compelling compared to an intelligence that takes 1000 years to return "42"
I look at it this way. A year ago is a long time in AI terms, but not that long. Those models were already decent for the tasks we're using SOTA models for today.
So imagine taking a year-old SOTA model and running it at 100 tokens per second on an edge device. That's enough to feed a screen's worth of content through it and power decent multilingual message suggestions on IM.
Imagine running it at 1000 tps. That's enough to reparse that screen mid-keystroke, and give you semantic autocomplete in text. Or fully general "the phone has a good idea of what you're attempting to do" context at all times.
There's many, many new classes of features that will open up if decent enough models can be run on edge devices at 100+ "intelligence per second".
Or vice versa... 1000tps == 1tps via solar power + a capacitor (or whatever).
I'm poking (lightly) at the locallama game, have a fancy MBP5 with gobs of ram (so I can demo/trial locally) and have been semi-waffling between whether to chase a mini or studio for local "always on" type stuff.
My outcome was "CapEx v. OpEx", and dropping another $5k for an aluminum cube buys a lot of OpEx (eg: just trickle-drip HF/OpenAI credits to a raspberry pi or VPS orchestrator rather than trying to do the inference locally), ie: $5/mo inference for 1000 months.
HOWEVER, there's definitely a role for that 1-10 TPS type "ambient inference" that I wouldn't mind sustaining on any sort of always-on / local / private compute. My main email address is still on ...@yahoo.com and their spam filtering has gone to absolute shit.
Being able to have the always-on mini (local, trusted, no private data leaves my control) poke at the IMAP/email and thresh it into SPAM/HAM/Personal/Political ... random spot check, I'm getting ~5 emails per hour, and that's completely tractable for staged low-med-high processing. (Subject + rules.py? Subject + Body + LocalSlowTPS? Subject + Body + LocalDeepTPS? Subject + Body + RemoteLLM?)
Even if you did 10000tps of "jev" that's an incredible value... not quite "Literal AI Packet Router", but as you're dancing aroud saying... "Speed is a Weapon"
Look into "OODA Loop" => """The OODA loop is a four-step decision-making model—Observe, Orient, Decide, Act—created by U.S. Air Force Colonel John Boyd to help leaders make fast and accurate choices in chaotic situations. // The main goal is to cycle through the loop faster than an opponent or changing environment. By operating inside another person's loop, you create confusion and outpace their ability to respond. While initially designed for aerial combat, it is now widely used in business, sports, and crisis management."""
It is still a bet. Is the current model still going to be good enough next year? We have no idea what will change. If the change is minor improvements than current fable on a cheap is cheaper and better than next years sonnet on GPU. However there are plenty of things people want that maybe they will deliver and suddenly I wouldn't touch today's fable when I can run next years sonnet instead.
But I think it's a good bet. I think that in two years, if I can get opus/sonnet 5.5 or the gpt-6 models for much cheaper and faster than whatever the "frontier" is at that point, that this will probably be a great trade for most of my work. I certainly don't know that for sure, that's why it's a bet, but it's what I think right now.
I wouldn't quite say that about any of the open weight models at this point. But I'm hopeful that will change in the next generation or two of those models.
Also "faster" indirectly means "more intelligent" with reasoning models.
For example, Opus 4.6 Max was somewhere between Opus 4.7 Medium and High in some benchmarks, but it was slower. If there was a way to run it 10x faster, the economics would be different.
From what I've read, not only are some labs doing it (other commenters already mentioned).
But it's complicated for other reasons, one being that the number of parameters for frontier models (especially with MoE models) are so high, and not always utilized (once again, thanks to MoE) that it would actually be incredibly cost prohibitive, if not impossible, to attempt to make giga-chips that would allow running it.
I definitely do believe that we will see more and more specialized chips over time, but putting the entire model on a chip is still a ways away.
I believe Taalas has a heavily handicapped llama 8-billion parameter model. And it still pulls >200W to run.
I can't imagine how anthropic or open ai would be able to burn a multi-trillion parameter model on a chip, we just aren't there yet.
Yes, and startups do exactly that. Check out Etched https://www.etched.com/ where they made a Transformer specific GPU (basically a form of ASIC) where they bet that transformers would be the dominant GPU architecture for running AI / LLM workload.
The bottleneck, for inference at least, is memory bandwidth. And that you can't make any faster by making it specific to your model.
So companies try to maximize the memory bandwidth they can get, balancing tradeoffs of power/area/programability of their chip. Right now they feel like the economy on power/area is not worth the decrease in programability/flexibility.
Presumably though the kernel has a pretty specific set of operations done against the weights in memory. Burning the weights into the memory with local memory cores capable of the kernel operations would be a lot more efficient than round tripping busses.
The primary constraint isn’t likely what’s possible to do, but that the kernel and weights are too variable right now and the patterns too poorly established to bake into hardware accelerators yet. Margin pressure is also not there yet.
I suspect as the marginal utility of the frontier improvement settles into diminishing returns (I suspect we are there already tbh) baking hardware models with ROM, working set, and kernel cores collocated will be the frontier space as the goal will become reducing capital spend to utility levels rather than research levels.
Once someone has a model that is sufficient for almost any practical use, making marginal inference cost effectively zero will be the competition frontier. I do shed a tear for all those lonely data centers as compute densities will almost certainly make most of them a terrible investment.
You're starting to hint at compute-in-memory as a general replacement for CPU/DIMM layouts. That could be useful for far more than LLMs, world models, or any sort of AI. It takes a bit of a different software development stack than a standard architecture though.
> Burning the weights into the memory with local memory cores capable of the kernel operations would be a lot more efficient than round tripping busses.
Sure, but now we're not talking about just burning the weights into the chip, but also designing a new architecture that has memory local to each core. A new architecture would then require a new programming model, which means new inference stack, which may mean new training stack.
I don’t think it would require a new training stack, and I’d imagine it makes more sense to distribute cores with memory. The cores can be simplified to the functions of the kernel since the inference kernel can be expressed as a reduced set of optimized functions in the pipeline rather than a general CUDA core. If the model is burned into ROM, the compute pipeline can be baked into the core.
I think one limiting factor here is that Big AI cannot focus on building solid, stable products that do a job well. It's okay if it happens incidentally, but doing it on purpose would be self-defeating.
Their stock price, be it public or estimated, is heavily pricing the notion that they are first and foremost Growth companies. Therefore their focus must remain on ever better and greater things. If they lose focus and get distracted by lesser endeavors, their valuations crumble, their ability to raise capital vanishes, and their runways collapse before they ever have a chance to reach their end goal, whatever that may be.
That means the boring job of productizing AI models into reliable systems that won't vanish in six months is left for a smaller company willing to pick up the crumbs. Unless they get acquired by Big AI before getting it done.
As a SW person (working with silicon folks though), I was always interested in the same idea. So I just vibecoded a little compiler from LLM (config.json, weights) to RTL (happy to share if you want). Then synthesized it via yosys.
I picked TinyStories-1M, synthesized via yosys against SKY130 PDK. It's GPT-Neo with 8 layers. At int8 it's ~30mm^2 per layer (including kv caches, multipliers and all the attention stuff). So it's 250 mm^2 for the full toy model.
I'm kinda surprised, I spent no time, so if you want to etch a toy model, you can do that (you can probably reduce the area significantly). If you want a mask for that is probably going to cost 100k though.
Would you pay to crystalize one of today's models in silicon so you can use it in 2028, or would you wait for another 6 months to see how models improve before pulling the trigger on that kind of commitment?
If I controlled a budget like this, I think I would put some portion of it toward paying to crystallize one of today's models in silicon, yes. Not 100%, but I do think this makes sense to invest in at this point. I would not have said so a year ago.
Naive question: would flash or some kind of write once memory be dense enough to replace a mask ROM? I'm wondering whether it is feasible to manufacture "blank" chips at the semiconductor factory that have a fixed architecture, but are model-agnostic. These chips would get the then-current weights burned in on first use. It's probably quite wasteful, but it could keep the same chip design alive for a longer time.
DRAM seems to sit in the economic sweet spot of density and bandwidth. I'm not sure if any other storage technology can provide non-volatile or one time programable storage with either the same density and bandwidth. Although I must say, it seems insanely wasteful to use RAM to store model weights, there must a better solution.
The closest anyone has gotten is Cerebras with their Wafer Scale Engine. It uses SRAM embedded with the compute. A single chip is an entire wafer, but the headline spec, how much ram, only 44GB, which is tiny for the silicon area used.
I understand traditional IC production workflows are ludicrously expensive, and glacially slow, but surely at some point the economics are going to tip in favour of mask rom.
Say you setup your foundry/packaging/ai chip facility. You come up with a new set of model weights. Run your CI/CD pipeline to produce a new mask output. The only thing you've changed are the assignments of the bits, this is extremely low risk change. The new masks should be completely interchangeable with the current process.
You produce the new masks, swap them into your foundry process, and all of a sudden your new chips have the latest version of the model. This would probably manifest itself in the form of inference providers having yearly / bi-yearly "updates" to their models as new hardware is brought online.
Tiered subscription levels would gate keep access to the latest and greatest model, cheaper subscriptions will be limited to older versions of the model, and so on, until running the hardware is no longer economically viable (no demand/running costs exceeding what the market is willing to pay).
I think Taalus is a bit reluctant to publish precisely how they do it but vaguely:
> We basically have an architecture where we are embedding the models, and we are hard coding the models and the weights into our what we call the mask ROM recall fabric, which is paired with an SRAM recall fabric. Together, they are able to store both the model as well as do all the computations of KV cache. We have adapters and customizations – we support all of that. This design allows us to be super-dense in terms of compute and in terms of storage, and we can do compute on that storage incredibly fast, which is what drives density up and cost down
Why is everyone here is talking "improving silicon" == "baking in the model weights into hardware"
what happened to improve existing hardware in the ways not obvious to humans? Some novel transinstor placement, some new approach to make logic gates faster, matrix multiplications etc - the filed is huge
It will happen once models get good enough for specific applications like robotics; there the hardware is fixed and power requirements are strict, so it makes sense to have dedicated silicon. But for use cases that doesn't require onboard, it's always better to get access to the latest and greatest, until we get to the point where improvements are marginal between model releases (we're not there yet)
Its coming, AMD snapped up the company behind https://chatjimmy.ai/ Taalas that did exactly that with an older model. As others have been saying the point is to know when to do it. At what point is there a model that cannot drastically improve that you can then do this lets say for the mid and low tier models leaving frontier to run on gpus.
We are still in the middle of the AI race. Commodity hardware is easy to use, can do everything and is fast enough.
Your optimized hardware chip might be obsolete before its back from the fab.
SOTA Frontiermodelhardwarechip is a benchmark point of a potential model slow down.
Google is doing it right now under project Frozen v2 which should be ready by 2028? which is either just a small experiment or flexible enough and thats why it takes so long for it to happen.
This question would be better answered if people were careful about distinguishing between "models" and "transformer architecture".
If you bake a given transformer architecture into silicon and then, a year later, changes in transformer architecture give a large inference performance boost, you may have to throw away all that now nearly-useless silicon that gets outperformed by humble GPUs.
Models fully deprecate in a few months. Why would you burn an algorithm that fully depreciates in value faster than a bag of potato chips. The 'inefficient' general purpose hardware is constantly renewed with every released model. Even 6 year old Ampere GPUs are still usable.
Because the iteration speed on models is so fast that by the time they have an ASIC ready for one model version, they are already significantly ahead in capability. Think of how big the jump between Opus 4.8 and 5.5 has been. They were released four months apart.
Cuz once they're in chips they can be put into robots, and once they're in robots it won't be so easy to reach the off switch, and once we can't easily reach the off switch, we're doomed.
A hybrid approach might work better for robotics. A model-in-silicon for body control and sensing, then a different model on processors for reasoning, speech and decision making.
What makes a ROM hard? I'm ignorant of the details of chip design, but I'd expect just a large array of essentially open or closed connections. Is that incorrect?
plus row and column decoders that get more and more complex with size. but consider the size of the weights. convert to mm^2. Gawk at the resulting value
Qwen 3.6-27B is 56 GB of weights. Let's round to 550Gbit. NOBODY makes huge ROMs in modern SoCs, but if we assume it is as dense as the densest TSMC N3 SRAM, this is 33.5 Mbit/mm^2
Then your weights are 16,400 mm^2 !!! The maximum reticle-size N3 die is ~800 mm^2 so you cannot make it.
But, you might say, SRAM is 6 transistors. ROM might be just one. So 1/6 of that... 2730 mm^2... which is still too big to manufacture.
GPUs are faster, but you can't make your own arch on GPUs. FPGAs offer you that possibility. Said that... There are a few beasty FPGAs used in crypto mining coming my way... I expect that OpenTPU will be able to run frontier models with those.
First opensource arch that can do modern LLMs, while maximizing the potential of its hardware;
First opensource TPU build by a recursive improvement loop...
It's upcoming second generation could run the inference of the models that are being used to improve it...
It’s not. Do it as a hobby or for privacy but for performance just use a frontier model api. You’re paying less than cost for something that would take tens of thousands to set up locally.
That's not even remotely close to being true, even once you account for capex. You have to look at the actual usage, look at the token limits. Even if you're paying Anthropic $200k/month for scale-tier, you're going to blow through your token limits trying to run max output 24/7. Three users running Opus 4.8 at max non-stop will probably clean your monthly allowance from daddy Dario in less than a week.
With an 8x MI355x cluster at full tilt and including cooling, your power draw runs ~17kW. That's what it looks like when it's running full tilt. To be fair, hey that's pretty expensive. It does mean 8 multi-trillion parameter models unquantized running 24/7 without pause. And you get the full month like that, your monthly token limit is the time in a month. That cluster, the electrical upgrade, the cooling setup, and the electricity to run it all costs less in 2 months than your maximum affordance from Anthropic does in the same time period. Two billing cycles, and realistically it's more like two weeks. In 4 quarters you've wasted over a million. Like, what are we talking about here?
Now if you aren't using AI all that much, which is perfectly valid, and especially if you aren't using it at its absolute maximum, the story changes. Because even though at that point you're not paying nearly as much in electricity to run the cluster anymore, you still have the $300k+ capex to get the setup in the first place. But if we're not redlining it non-stop, then we're not really talking about performance anymore, are we? If your org never comes close to hitting token limits, it's probably because AI is rather marginal for you. Which again, is perfectly valid. I don't even use AI professionally.
Fact of the matter is, if your corp can justify the capex for a cluster and makes heavy use of AI, you are literally burning money by not having one in your building. The numbers are painfully obvious. Even deepseek isn't as cheap. This is before we get into things like LoRAs, custom inference pipelines, etc. which you know are kind of important if you actually care about model performance.
> With an 8x MI355x cluster at full tilt and including cooling, your power draw runs ~17kW. That's what it looks like when it's running full tilt. To be fair, hey that's pretty expensive.
Pretty expensive is an understatement. You couldn’t buy one of these if you wanted to right now. If you could it would be multiple hundreds of thousands of dollars.
> It does mean 8 multi-trillion parameter models unquantized running 24/7 without pause
You can’t even run one unquantized multi-trillion parameter (>=2T) model on 8 x MI355x with enough context for concurrent users. I don’t know how you think it’s going to run 8 of them at the same time. Did you mean 8 concurrent sessions?
Your math is way off across this post. If replacing an Anthropic subscription for a whole company was as easy as buying a box for the office and then breaking even in 2 months, it wouldn’t be some little secret that we only discover in a comment online.
> Pretty expensive is an understatement. [...] If you could it would be multiple hundreds of thousands of dollars.
Obviously, I quantified both the operating expense and the capital expense in my post. What I find curious is that you're quoting me talking about the operating expenditure, and changing the topic to be about the buy-in like these are interchangeable things. You don't think that this is a crucial and important distinction?
> You couldn’t buy one of these if you wanted to right now.
You could have spent all of 5 seconds of searching rather than just assuming[1]. You're not buying an Nvidia Superpod™.
> You can’t even run one unquantized multi-trillion parameter (>=2T) model on 8 x MI355x with enough context for concurrent users.
That's certainly fair a point. Although in the English language, especially in legal contexts, the multi- prefix is used inclusively for fractional values. That is it's strictly >1, not >=2. IE an 18 month contract is a multi-year contract, or a $1.6 million dollar asset is a "multi-million" dollar asset. But this is uninteresting semantics.
You are right, but it also doesn't matter. The gap is just that big. You can run 1 single user of Kimi K3 and still not even come remotely close to the $70k or so that a single Opus 4.8 user can burn over the course of a month on left on max. An honestly lowballed amount I know from anecdote. The per-token cost is just really expensive.
> Your math is way off across this post.
You made one technical point above, one that doesn't ever arrive at a relevant rebuttal to the substance of my post. But please, I'd love to hear you elaborate, especially because I didn't actually give much math at all.
If you want math though, here's the math. Let's say you are paying a ridiculous amount of money for electricity, a price nobody in the US pays -- $2 per kilowatt hour. That's about 5x the average rate in California, 4x as in Hawai'i. 17kW @ $2/kWh * ~8766 hours in a year puts that cluster's electrical costs at just shy of ~$298k annually assuming it takes no breaks. Let's make matters worse and round that up to $300k. It's also assuming you didn't invest in a solar hookup for your building, which I don't know why you haven't at this point, especially if you're installing a CDU for your new cluster. 12 months of Claude burning $70k a month is $840k. For a buy in of, you know what, let's call it $500k. Why not? It still doesn't matter. The operating cost is so much lower it's paid for itself plus an additional $40k in the first year. Even at a ridiculous penalty in electricity that nobody pays, even overinflating the amount of money you'd pay for the cluster and the infrastructure to get it set up, it's not even remotely close for a single user where the gap is smaller (IE, you're not wasting "a million dollars" in a year by maxing out the $200k scaling limit every month)
You can of course trot out the point that oh, in 12 months this setup will be extremely outdated! It doesn't matter. If the work it was doing today was useful, it will be useful next year too. And with the rapidly encroaching diminishing returns from parameter scaling, you're probably going to be just fine for a while. Maybe grab a quantized version of a newer Chinese model at the end, before grabbing a newer generation of AMD node. Those MI400s are looking pretty sweet after all.
> If replacing an Anthropic subscription for a whole company was as easy as buying a box for the office and then breaking even in 2 months
If you're locked in, then you're locked in. But don't pretend like you're saving money. You're not.
> it wouldn’t be some little secret that we only discover in a comment online.
Why does this have you so nasty and defensive? It's not a "little secret" that running your own infrastructure is cheaper. Of course it is. You know what else is cheaper? Owning your own office building out in the sticks, rather than leasing part of one in the city. Not everybody can make that work, there are no free lunches after all.
History repeats, these same exact lines were rolled out ad nauseum during the cloud craze. Datacenters are businesses, not charities. Frontier companies rent quite a fair amount of their infrastructure. Even if they resold that compute below cost (they don't), there's a pretty steep cliff before the economics start to look attractive.
> You could have spent all of 5 seconds of searching rather than just assuming[1].
I guarantee this will not ship to you any time soon.
The current lead time on these GPUs in measured in years. If you didn't place an order for this a long time ago, it's not coming this year.
Being able to add it to an online configurator does not mean anything right now.
> 12 months of Claude burning $70k a month is $840k
Your math is completely useless with these arbitrary numbers pulled out of the air.
If you want to begin calculating payback period you'd need to look at token costs, cost per task, utilization rates, and so on.
> The operating cost is so much lower it's paid for itself plus an additional $40k in the first year.
You went from paying back in a couple months to paying back in a year but you still haven't even talked about tokens or concurrency.
You're also neglecting the fact that hosted tokens are going down in price at a rapid rate. If someone was paying $70K per month in tokens for Opus this month, that same level of compute is going to be much cheaper 12 months from now.
> Why does this have you so nasty and defensive?
Not nasty or defensive, just tired of these armchair claims that it's easy to go out and buy an 8 X MI355X box from people who obviously have no idea what the hardware lead time is like right now, or who haven't considered the actual math on token costs and payback times. You're still making a lot of claims without a single discussion of cost per task or token.
It usually boils down to people trying to convince themselves that keeping their macs hot and with very little ram to spare only to get sub 50 tokens per second on a subpar lobotomized (quantized) model is worth it.
And I'm not even considering their time spent fiddling, fine tuning configs to adjust for ram, updating/benchmarking models, etc. Which is probably more expensive than the mac so the math is even more wrong.
> I guarantee this will not ship to you any time soon.
The assumption, the starting point, is that you have a line on the hardware. Asking around, some distributors have a 6 month lead time on Instinct GPUs, which curiously enough is about how long you'll be twiddling your thumbs waiting for the cooling loop to be put in. Yes things take time.
> Your math is completely useless with these arbitrary numbers pulled out of the air.
Your dismissal is worthless if you can't even be bothered to provide a counter-example. You've not provided a single iota of quantified reasoning beyond my original not accounting for the space used for the context of concurrent users.
> If you want to begin calculating payback period you'd need to look at token costs, cost per task, utilization rates, and so on.
Now go back and carefully reread my original post. Yes, if you are not actually redlining an LLM for a billing cycle, the capex starts to be way more relevant for this setup. Otherwise, our constraint is time and our unit of measure is $/hr.
If you want to compare token cost, it may shock you to learn that Kimi K3 without speculative decode on this setup is slightly under twice as fast as Opus 4.8 max. That's still true when fast is compared with K3 with speculative decode, and now Claude is twice as expensive as a base rate. Oops. We're already burning more money over a period of time, looking at tokens we're screaming even further ahead.
> You're also neglecting the fact that hosted tokens are going down in price at a rapid rate.
Cool. Call me when Opus 4.8 max is $0.50/million. In 4 years you could have bought the 200 acres of land down the road from your building, started a 5MW solar farm subsidiary that you'll expand over time, and as soon as your connect is up, dropped the opex of the cluster down to its maintenance costs. That subsidiary will pay the loan required to spin it up back irrespective of your primary business. When you own your own shit, you can play your own game, stack your cards deep. Have a little bit of business acumen. Fuck what The Valley is doing, that is an ecosystem fully enslaved by economic nihilism, money isn't grounded there.
> Not nasty or defensive, just tired of these armchair claims that it's easy to go out and buy an 8 X MI355X box from people who obviously have no idea what the hardware lead time is like right now
This motte-bailey routine is both nasty and defensive, particularly when you keep prosecuting a geist of numeric justification that never arrives. All I've gotten from you is vague dismissals, one borderline irrelevant technical argument, moving goalposts and missing the point. Granted, not as egregiously as other people in this chain thinking we're talking about running 100B models on a Mac, I'll give you credit for that. But this whole time, we're just talking past each other. You make realistic points and I try to bring you back to context, but you have to work with me here too.
The point was that these companies are not selling to you below cost, they're not even selling to you at-cost. Just use your head. Venture capital isn't a magic wand. Frontier companies are in the red because they're in non-stop expansion operations at massive scales. Anthropic has an operating profit of half a billion dollars[1]. They are not selling you API usage below cost.
"You can of course trot out the point that oh, in 12 months this setup will be extremely outdated! "
But interestingly still extremely valuable on the second hand market.
The capital expense isn't the amount laid out. It's the rental cost of obtaining that capital, less the depreciation on the fixed asset over the period in use.
Going back to the OP, Apple gear is well know for having good resale values, which means the capital outlay isn't anywhere near as much as some people think.
>You couldn’t buy one of these if you wanted to right now.
You can: https://www.exxactcorp.com/Exxact-TS4-149591758-E149591758 . You can get thousands of tps of GLM 5.3 output out of this thing, which grades around Opus 4.8. Payoff is around 1 year vs. spot prices on these GPUs, including power.
Ah thanks for the solid info, too bad. I'd seen them come up as a pretty good price for 6000 RTX's in the past, which seem generally pretty available, good source for those?
Yeah, they're good source. But the price for those GPUs is 5 figs even with the nvidia startup program nowadays. Also, I went back and looked. Most of my GPUs are actually from Central Computers who were great, but Exxact is real too. So "lots of" was inaccurate.
Also, the lead time I quoted was for individual 8x nodes.
I can't tell from the ad -- it says "supports" 8x MI350X GPUs, but does that mean "includes" 8x MI350X GPUs? For $300K I'd certainly hope so, but I'm assuming not.
A system with 4x RTX 6000s costs about $60K these days, and can (as you note) trade blows with Opus 4.8 if not Fable. In fact, it'll give you a better pelican than Fable 5.1, and in less time.
Well, they are if you're into animating pelicans. :-P But yes, in the general case Opus is a better match.
And Opus is no slouch. I'm satisfied that GLM 5.3 is just as strong as Opus. Z.AI has promised/bragged that they will be at Fable 5.0 level by the end of the year or early next year, and I don't see any reason to doubt them.
Baseline yeah. But part of the reason you run open models is how much nicer fine tuning them is. Granted, you probably don't want to try and make LoRAs on a 4x RTX6000 setup, but you could if you really wanted to and there are other ways to modify models. And yes, if you're good at it, you can turn a piddly mid-range model that's only good at benchmarks into a heavyweight clanker (for a specific domain).
Ha fair, I'd definitely confirm with a salesperson before wiring them $300k. But most of the signs on the configurator seem to point to it including the GPUs? Not going to make 30k BTUs/hr of heat without the 8kw of GPUs.
Here’s an experiment: purchase an anthropic pro max subscription for $200/m. Now go buy the hardware to run DeepSeek’s equivalent. In a year, who spent more?
In normal times in which hardware used to depreciate (lately that's not the case and HW even appreciates, but let's not get distracted), if you calculate only with depreciation costs, plus the fact that when you have such a setup, it'd take many 200$ subs to cover your lack of limits in the other, I think it'd not be a clear victory for any side.
If you just ask "who spent more in the first year" (100% depreciation) then even with 5-6 max accounts, buying HW will be a couple of times more expensive. But when does it make sense to ask that question?
Maybe the SotA models will need better hardware so your investment will not be useful after a year or you'd need very expensive upgrades? But then (as in Fable case) subscribers need to spend more too.
It’s not so clear after 5 years that you’ll come out ahead. You’ll have spent $20k. The apple computer owner will probably be running local models that are better than today’s frontier on the same hardware.
Idk where you live, but where I am running the M5 Ultra Mac Studio at max rated power 24/7 for a month costs C$42.
The considerations against Apple hardware are 1) hardware advancements 2) early access to the best models. But it’s really not that clear.
(The other guy who thought hosted models on openrouter are cheap has spent $100k in 5 years.)
Open weight models have been getting better/smaller every year.
Also, from what I can tell, MLX inference is not as well optimized as CUDA, and the M5 Ultra has additional kinds of AI compute which is unavailable on other M models. With the massive 1.2 TB/s 512GB Mac studios coming out, I think MLX will get a lot more attention.
In short: Todays models should run faster next year, and next year's models should also be more efficient.
It does make me wonder how the hosted stuff is so cheap. For pretty much everything else, hosted/rented is more expensive but offers better convenience and flexibility. But for AI, even if you consider the total lifetime cost and are utilizing it heavily. You never break even by buying.
They're not cheap at all. I did one xhigh Qwen 3.8 27B agentic coding task last week via OpenRouter and it cost me like $10.
99% of the cost was in input tokens, I only used like 100k ish output tokens. It was a one shot task asking the agent to implement proxy injection to Guice. It did a pretty amazing job.
If you were to use hosted LLMs for a lot of agentic coding, a maxed out M5 Ultra Mac Studio would pay for itself in under a year.
I've been hosting Qwen3.8-27B myself. On my endpoint it's $0.30/1M in, $0.10 cache, $2.03 out - so those agent turns that re-send the same prefix get a lot cheaper when cache hits. UI at inference.tiyuvta.ai/app if you want to try it. Hosted is up to 210 tok/s and 280ms TTFT with reasoning off.
Qwen is weirdly expensive. Deepseek v4 flash is dirt cheap. You'd need at least 128gb of ram to run this model and in my experience, a days work with it costs around 80 cents.
So I ran the math, assuming the agent takes 75 turns per 200k context, with deepseek v4 flash it costs around $2.57 to reach 1M context in 375 turns. Cached input costs scale quadratically with # of agent turns.
Considering that I hit the 1M compaction multiple times per day with codex, it would definitely cost at least $5-8/day to use deepseek how I normally use codex.
Here's the thing that's a little different about data centers; we can tell from Anthropic and OpenAI that they're capacity constrained. Inference demand is there. I notice Cerebras doesn't offer much directly any more, all their capacity is getting completely sucked up by B2B sales. Grok did overbuild, but Anthropic was so desperate for more compute they ate their pride and leased the excess capacity.
That means all these data centers are being heavily utilized by actual end user inference demand. Well, some is research on new models, but a lot is actual end user demand. No one has given an explanation of why peoples usage would decline.
On top of that, margin on inference appears to be decent. It's model training that's a serious financial burden.
And maybe that's where there will be a slowdown, maybe the market doesn't justify spending as much on R&D as it does, but the end demand for inference is there.
Does that justify these stock prices? That's a different question. But the housing boom left behind endless rows of empty homes because demand disappeared. The 'dot com' boom left behind thousands of miles of dark fiber that'd been built out well ahead of demand for bandwidth. I can see the stock market having a giant sell off, but I don't see data centers sitting idle in that same fashion.
The number of planned data centers and the scale is pretty nuts.
Here's what you have to believe:
- AI demand is at least several times larger than what can currently be satisfied, or will grow. (This one I can buy, but...)
- AI chips (GPUs, TPUs, compute-in-memory, whatever else is being studied) will not get significantly more efficient than they are now. It will not be possible in, say, 5-10 years, to do 2X or 4X or 10X more AI requests per rack than is possible now. I think this one's the single most likely thing to be false, since all computing history contradicts it.
- Edge devices (PCs, laptops, specialized but smaller scale AI compute nodes) will never be powerful enough to run frontier models at a reasonable price that's appealing for professionals, enthusiasts, or businesses, and there will never be a market for this. None of the demand will be served on-device or near-edge. AI must all go in giant data centers.
- AI models will not become significantly more efficient than they are now. There are no large gains on the table from better model architectures, better training, more efficient quantizations, better harnesses, etc.
If all those things are true, than the current planned like 4X-10X increase in data center capacity makes sense. If even one or two of them are not true, then the planned data center build-outs start looking excessive. If all four are not true, it's a total bubble that will crash and burn. Answer is probably somewhere between, but how far toward bubble? That's why I picked a number like "only 20% ever gets built." It might be as high as 50%. It ain't gonna be 100%. The planned built-out is batty.
Oh I forgot two more...
- Data center capacity currently serving non-AI work loads does not shrink through either reduced demand, more efficient software, or (most likely) faster chips and denser RAM. If that happens, more pre-existing DC space can serve AI work loads.
- Orbital solar powered compute nodes never happen. If this happens (free power! much less political opposition!) then terrestrial data centers have significant competition.
Honest question. Who got to decide to accept this decision on behalf of state and federal citizens? This seems performative rather than punitive and doesn’t seem to match the harm.
My father was a plumber in the 80s and bought our family an Amiga 1000 for $1,200. Today thats about $3800 in inflation adjusted money. I just bought my son a top of the line pc for $2,000. I get that it used to be cheaper, but value is still relatively good for these machines.
In 2011 I bought a fully loaded 27 inch iMac paid 3700 (used it for 9-1/2 years) at today’s prices that would be about $5400, bought a M2 Mac studio M2 ultra that cost about $7200 2-1/2 years ago the price for the new Mac studio M5 ultra just announced by Apple is $11,400.
Former owner of Amiga, 1000, 2000, 3000, and 4000 I would have gotten a Next computer at the time if I could’ve afforded it, unfortunately, I was tied to a PC at work using AutoCAD, Revit and Navis Managed a few years later.
The Games back in the day on the Amiga computers were ahead of the PC for about 9-10 years, coincidentally Sony bought a British gaming company called Psychosis for their games and gaming technology. They were pretty big back in the day on the Amiga.
Except we've gotten so much better at making computers since the Amiga, its a travesty that things have gotten so bad for consumers. You know the ones that bail out the bubbles
They have a vision which consists of computer users being tied back into mainframe computing with them as the gatekeeper. That is the wet dream of Microsoft, OpenAI, Anthropic, Nvidia, Google along with Meta.
The thought of local AI is also disgraceful, Nvidia didn’t buy for Hugging Face for 12-13 billion for nothing.
Well said. I hope some day to make it and start parent.capital a place where parents can get funding. I believe there are thousands of people out there with great ideas and immense talent who are unable to leave their salary and benefits behind to pursue them.
When you become a parent, your risk profile changes immensely. I can hire some of the world’s best people tomorrow if i can promise them equal salary, benefits and retirement accounts for a set number of years. Plus the benefit of a large upside if it works. Most startups offer lower salaries and abysmal benefits. Security matters.
I still don’t get it. It sounds like the business plan is to fund companies so they can offer compelling employment. That’s great but that’s no different than any other VC.
Thanks for all your open source work. You changed the industry for the better. Are you thinking of building a Github competitor? I would find that very appealing.
If you’re going this far, why even have a cli? Just have your agent grab the openapi spec from the server and set envvars. It can curl its way around happily. Security? I don’t see how this is any more or less secure than any other claude code workflow.
It seems we've round about rediscovered apis.
I tried this with datadog and it can build high quality monitoring notebooks for the pr im working on in a single shot.
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As I'm on vacation, I took a couple days to put together an idea I've had bumping around in the back of my head for a while. What if you could write pure ruby methods and ask an llm to figure out all the implementation details to return the response to any method call?
The library leans hard on ruby's magical (hence the name) `method_missing` which has always impressed me with its simple cleverness.
This is my first open source project, so please leave feedback and be kind.
