The path to ubiquitous AI (17k tokens/sec)
taalas.com319 points by sidnarsipur 3 hours ago
319 points by sidnarsipur 3 hours ago
This is not a general purpose chip but specialized for high speed, low latency inference with small context. But it is potentially a lot cheaper than Nvidia for those purposes.
Tech summary:
- 15k tok/sec on 8B dense 3bit quant (llama 3.1)
- limited KV cache
- 880mm^2 die, TSMC 6nm, 53B transistors
- presumably 200W per chip
- 20x cheaper to produce
- 10x less energy per token for inference
- max context size: flexible
- mid-sized thinking model upcoming this spring on same hardware
- next hardware supposed to be FP4
- a frontier LLM planned within twelve months
Certainly interesting for very low latency applications which need < 10k tokens context. If they deliver in spring, they will likely be flooded with VC money.
Not exactly a competitor for Nvidia but probably for 5-10% of the market.
Back of napkin, the cost for 1mm^2 of 6nm wafer is ~$0.20. So 1B parameters need about $20 of die. The larger the die size, the lower the yield. Supposedly the inference speed remains almost the same with larger models.
Interview with the founders: https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...
This math is useful. Lots of folks scoffing in the comments below. I have a couple reactions, after chatting with it:
1) 16k tokens / second is really stunningly fast. There’s an old saying about any factor of 10 being a new science / new product category, etc. This is a new product category in my mind, or it could be. It would be incredibly useful for voice agent applications, realtime loops, realtime video generation, .. etc.
2) https://nvidia.github.io/TensorRT-LLM/blogs/H200launch.html Has H200 doing 12k tokens/second on llama 2 12b fb8. Knowing these architectures that’s likely a 100+ ish batched run, meaning time to first token is almost certainly slower than taalas. Probably much slower, since Taalas is like milliseconds.
3) Jensen has these pareto curve graphs — for a certain amount of energy and a certain chip architecture, choose your point on the curve to trade off throughput vs latency. My quick math is that these probably do not shift the curve. The 6nm process vs 4nm process is likely 30-40% bigger, draws that much more power, etc; if we look at the numbers they give and extrapolate to an fp8 model (slower), smaller geometry (30% faster and lower power) and compare 16k tokens/second for taalas to 12k tokens/s for an h200, these chips are in the same ballpark curve.
However, I don’t think the H200 can reach into this part of the curve, and that does make these somewhat interesting. In fact even if you had a full datacenter of H200s already running your model, you’d probably buy a bunch of these to do speculative decoding - it’s an amazing use case for them; speculative decoding relies on smaller distillations or quants to get the first N tokens sorted, only when the big model and small model diverge do you infer on the big model.
Upshot - I think these will sell, even on 6nm process, and the first thing I’d sell them to do is speculative decoding for bread and butter frontier models. The thing that I’m really very skeptical of is the 2 month turnaround. To get leading edge geometry turned around on arbitrary 2 month schedules is .. ambitious. Hopeful. We could use other words as well.
I hope these guys make it! I bet the v3 of these chips will be serving some bread and butter API requests, which will be awesome.
Think about this for solving questions in math where you need to explore a search space. You can run 100 of these for the same cost and time of doing one api call to open ai.
I think the next major innovation is going to be intelligent model routing. I've been exploring OpenClaw and OpenRouter, and there is a real lack of options to select the best model for the job and execute. The providers are trying to do that with their own models, but none of them offer everything to everyone at all times. I see a future with increasingly niche models being offered for all kinds of novel use cases. We need a way to fluidly apply the right model for the job.
Agree that routing is becoming the critical layer here. Vllm iris is really promising for this https://blog.vllm.ai/2026/01/05/vllm-sr-iris.html
There's already some good work on router benchmarking which is pretty interesting
For speculative decoding, wouldn’t this be of limited use for frontier models that don’t have the same tokenizer as Llama 3.1? Or would it be so good that retokenization/bridging would be worth it?
My understanding as well is that speculative decoding only works with a smaller quant of the same model. You're using the faster sampling of the smaller models representation of the larger models weights in order to attempt to accurately predict its token output. This wouldn't work cross-model as the token probabilities are completely different.
Smaller quant or smaller model?
Afaik it can work with anything, but sharing vocab solves a lot of headaches and the better token probs match, the more efficient it gets.
Which is why it is usually done with same family models and most often NOT just different quantizations of the same model.
I think they’d commission a quant directly. Benefits go down a lot when you leave model families.
The guts of a LLM isn't something I'm well versed in, but
> to get the first N tokens sorted, only when the big model and small model diverge do you infer on the big model
suggests there is something I'm unaware of. If you compare the small and big model, don't you have to wait for the big model anyway and then what's the point? I assume I'm missing some detail here, but what?
Speculative decoding takes advantage of the fact that it's faster to validate that a big model would have produced a particular sequence of tokens than to generate that sequence of tokens from scratch, because validation can take more advantage of parallel processing. So the process is generate with small model -> validate with big model -> then generate with big model only if validation fails
More info:
* https://research.google/blog/looking-back-at-speculative-dec...
* https://pytorch.org/blog/hitchhikers-guide-speculative-decod...
See also speculative cascades which is a nice read and furthered my understanding of how it all works
https://research.google/blog/speculative-cascades-a-hybrid-a...
Verification is faster than generation, one forward pass for verification of multiple tokens vs a pass for every new token in generation
I don't understand how it would work either, but it may be something similar to this: https://developers.openai.com/api/docs/guides/predicted-outp...
When you predict with the small model, the big model can verify as more of a batch and be more similar in speed to processing input tokens, if the predictions are good and it doesn't have to be redone.
There’s a bit of a hidden cost here… the longevity of GPU hardware is going to be longer, it’s extended every time there’s an algorithmic improvement. Whereas any efficiency gains in software that are not compatible with this hardware will tend to accelerate their depreciation.
Were we go towards really smart roboters. It is interesting what kind of diferent model chips they can produce.
There is nothing smart about current LLMs. They just regurgitate text compressed in their memory based on probability. None of the LLMs currently have actual understanding of what you ask them to do and what they respond with.
If LLMs just regurgitate compressed text, they'd fail on any novel problem not in their training data. Yet, they routinely solve them, which means whatever's happening between input and output is more than retrieval, and calling it "not understanding" requires you to define understanding in a way that conveniently excludes everything except biological brains.
So you are saying they are like copy, LLMs will copy some training data back to you? Why do we spend so much money training and running them if they "just regurgitate text compressed in their memory based on probability"? billions of dollars to build a lossy grep.
I think you are confused about LLMs - they take in context, and that context makes them generate new things, for existing things we have cp. By your logic pianos can't be creative instruments because they just produce the same 88 notes.
We know that, but that does not make them unuseful. The opposite in fact, they are extremely useful in the hands of non-idiots.We just happen to have a oversupply of idiots at the moment, which AI is here to eradicate. /Sort of satire.
Thats not how they work, pro-tip maybe don't comment until you have a good understanding?
Would you mind rectifying the wrong parts then?
Phrases like "actual understanding", "true intelligence" etc. are not conducive to productive discussion unless you take the trouble to define what you mean by them (which ~nobody ever does). They're highly ambiguous and it's never clear what specific claims they do or don't imply when used by any given person.
But I think this specific claim is clearly wrong, if taken at face value:
> They just regurgitate text compressed in their memory
They're clearly capable of producing novel utterances, so they can't just be doing that. (Unless we're dealing with a very loose definition of "regurgitate", in which case it's probably best to use a different word if we want to understand each other.)
The fact that the outputs are probabilities is not important. What is important is how that output is computed.
You could imagine that it is possible to learn certain algorithms/ heuristics that "intelligence" is comprised of. No matter what you output. Training for optimal compression of tasks /taking actions -> could lead to intelligence being the best solution.
This is far from a formal argument but so is the stubborn reiteration off "it's just probabilities" or "it's just compression". Because this "just" thing is getting more an more capable of solving tasks that are surely not in the training data exactly like this.
Just HI slop. Ask any decent model, it can explain what's wrong this this description.
In 20$ a die, they could sell Gameboy style cartridges for different models.
Don’t forget that the 8B model requires 10 of said chips to run.
And it’s a 3bit quant. So 3GB ram requirement.
If they run 8B using native 16bit quant, it will use 60 H100 sized chips.
> Don’t forget that the 8B model requires 10 of said chips to run.
Are you sure about that? If true it would definitely make it look a lot less interesting.
Their 2.4 kW is for 10 chips it seems based on the next platform article.
I assume they need all 10 chips for their 8B q3 model. Otherwise, they would have said so or they would have put a more impressive model as the demo.
https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...
It doesn’t make any sense to think you need the whole server to run one model. It’s much more likely that each server runs 10 instances of the model
1. It doesn’t make sense in terms of architecture. It’s one chip. You can’t split one model over 10 identical hardwire chips
2. It doesn’t add up with their claims of better power efficiency. 2.4kW for one model would be really bad.
We are both wrong.
First, it is likely one chip for llama 8B q3 with 1k context size. This could fit into around 3GB of SRAM which is about the theoretical maximum for TSMC N6 reticle limit.
Second, their plan is to etch larger models across multiple connected chips. It’s physically impossible to run bigger models otherwise since 3GB SRAM is about the max you can have on an 850mm2 chip.
followed by a frontier-class large language model running inference across a collection of HC cards by year-end under its HC2 architecture
Thanks for having a brain.
Not sure who started that "split into 10 chips" claim, it's just dumb.
This is Llama 3B hardcoded (literally) on one chip. That's what the startup is about, they emphasize this multiple times.
It’s just dumb to think that one chip per model is their plan. They stated that their plan is to chain multiple chips together.
I was indeed wrong about 10 chips. I thought they would use llama 8B 16bit and a few thousand context size. It turns out, they used llama 8B 3bit with around 1k context size. That made me assume they must have chained multiple chips together since the max SRAM on TSMC n6 for reticle sized chip is only around 3GB.
> 880mm^2 die
That's a lot of surface, isn't it? As big an M1 Ultra (2x M1 Max at 432mm² on TSMC N5P), a bit bigger than an A100 (820mm² on TSMC N7) or H100 (814mm² on TSMC N5).
> The larger the die size, the lower the yield.
I wonder if that applies? What's the big deal if a few parameter have a few bit flips?
> I wonder if that applies? What's the big deal if a few parameter have a few bit flips?
We get into the sci-fi territory where a machine achieves sentience because it has all the right manufacturing defects.
Reminds me of this https://en.wikipedia.org/wiki/A_Logic_Named_Joe
2000s movie line territory:
> There have always been ghosts in the machine. Random segments of code, that have grouped together to form unexpected protocols.
Low-latency inference is a huge waste of power; if you're going to the trouble of making an ASIC, it should be for dog-slow but very high throughput inference. Undervolt the devices as much as possible and use sub-threshold modes, multiple Vt and body biasing extensively to save further power and minimize leakage losses, but also keep working in fine-grained nodes to reduce areas and distances. The sensible goal is to expend the least possible energy per operation, even at increased latency.
Low latency inference is very useful in voice-to-voice applications. You say it is a waste of power but at least their claim is that it is 10x more efficient. We'll see but if it works out it will definitely find its applications.
This is not voice-to-voice though, end-to-end voice chat models (the Her UX) are completely different.
I haven't found any end-to-end voice chat models useful. I had much better results with separate STT-LLM-TTS. One big problem is the turn detection and having inference with 150-200ms latency would allow for a whole new level of quality. I would just use it with a prompt: "You think the user is finished talking?" and then push it to a larger model. The AI should reply within the ballpark of 600ms-1000ms. Faster is often irritating, slower will make the user to start talking again.
Do not overlook traditional irrational investor exuberance, we've got an abundance of that right now. With the right PR manouveurs these guys could be a tulip craze.
An on-device reasoning model what that kind of speed and cost would completely change the way people use their computers. It would be closer to star trek than anything else we've ever had. You'd never have to type anything or use a mouse again.
This is insane if true - could be super useful for data extraction tasks. Sounds like we could be talking in the cents per millions of tokens range.
I've never gotten incorrect answers faster than this, wow!
Jokes aside, it's very promising. For sure a lucrative market down the line, but definitely not for a model of size 8B. I think lower level intellect param amount is around 80B (but what do I know). Best of luck!
Amazing! It couldn't answer my question at all, but it couldn't answer it incredibly quickly!
Snarky, but true. It is truly astounding, and feels categorically different. But it's also perfectly useless at the moment. A digital fidget spinner.
Holy cow their chatapp demo!!! I for first time thought i mistakenly pasted the answer. It was literally in a blink of an eye.!!
With this speed, you can keep looping and generating code until it passes all tests. If you have tests.
Generate lots of solutions and mix and match. This allows a new way to look at LLMs.
Is super fast but also super inaccurate, I would say not even gpt-3 levels.
It is incredibly fast, on that I agree, but even simple queries I tried got very inaccurate answers. Which makes sense, it's essentially a trade off of how much time you give it to "think", but if it's fast to the point where it has no accuracy, I'm not sure I see the appeal.
the hardwired model is Llama 3.1 8B, which is a lightweight model from two years ago. Unlike other models, it doesn't use "reasoning:" the time between question and answer is spent predicting the next tokens. It doesn't run faster because it uses less time to "think," It runs faster because its weights are hardwired into the chip rather than loaded from memory. A larger model running on a larger hardwired chip would run about as fast and get far more accurate results. That's what this proof of concept shows
I see, that's very cool, that's the context I was missing, thanks a lot for explaining.
If it's incredibly fast at a 2022 state of the art level of accuracy, then surely it's only a matter of time until it's incredibly fast at a 2026 level of accuracy.
yeah this is mindblowing speed. imagine this with opus 4.6 or gpt 5.2. probably coming soon
Why do you assume this?
I can produce total jibberish even faster, doesn’t mean I produce Einstein level thought if I slow down
I think it might be pretty good for translation. Especially when fed with small chunks of the content at a time so it doesn't lose track on longer texts.
I asked, “What are the newest restaurants in New York City?”
Jimmy replied with, “2022 and 2023 openings:”
0_0
Well, technically it's answer is correct when you consider it's knowledge cutoff date... it just gave you a generic always right answer :)
OK investors, time to pull out of OpenAI and move all your money to ChatJimmy.
A related argument I raised a few days back on HN:
What's the moat with with these giant data-centers that are being built with 100's of billions of dollars on nvidia chips?
If such chips can be built so easily, and offer this insane level of performance at 10x efficiency, then one thing is 100% sure: more such startups are coming... and with that, an entire new ecosystem.
I think their hope is that they’ll have the “brand name” and expertise to have a good head start when real inference hardware comes out. It does seem very strange, though, to have all these massive infrastructure investment on what is ultimately going to be useless prototyping hardware.
I dunno, it pretty quickly got stuck; the "attach file" didn't seem to work, and when I asked "can you see the attachment" it replied to my first message rather than my question.
A lot of naysayers in the comments, but there are so many uses for non-frontier models. The proof of this is in the openrouter activity graph for llama 3.1: https://openrouter.ai/meta-llama/llama-3.1-8b-instruct/activ...
10b daily tokens growing at an average of 22% every week.
There are plenty of times I look to groq for narrow domain responses - these smaller models are fantastic for that and there's often no need for something heavier. Getting the latency of reponses down means you can use LLM-assisted processing in a standard webpage load, not just for async processes. I'm really impressed by this, especially if this is its first showing.
Exactly. One easily relatable use-case is structured content extraction or/and conversion to markdown for web page data. I used to use groq for same (gpt-oss20b model), but even that used to feel slow when doing theis task at scale.
LLM's have opened-up natural language interface to machines. This chip makes it realtime. And that opens a lot of use-cases.
This makes me think about how large would an FPGA-based system to be able to do this? Obviously there is no single-chip FPGA that can do this kind of job, but I wonder how many we would need.
Also, what if Cerebras decided to make a wafer-sized FPGA array and turned large language models into lots and lots of logical gates?
Edit: it seems like this is likely one chip and not 10. I assumed 8B 16bit quant with 4K or more context. This made me think that they must have chained multiple chips together since N6 850mm2 chip would only yield 3GB of SRAM max. Instead, they seem to have etched llama 8B q3 with 1k context instead which would indeed fit the chip size.
This requires 10 chips for an 8 billion q3 param model. 2.4kW.
10 reticle sized chips on TSMC N6. Basically 10x Nvidia H100 GPUs.
Model is etched onto the silicon chip. So can’t change anything about the model after the chip has been designed and manufactured.
Interesting design for niche applications.
What is a task that is extremely high value, only require a small model intelligence, require tremendous speed, is ok to run on a cloud due to power requirements, AND will be used for years without change since the model is etched into silicon?
Where are those numbers from? It's not immediately clear to me that you can distribute one model across chips with this design.
> Model is etched onto the silicon chip. So can’t change anything about the model after the chip has been designed and manufactured.
Subtle detail here: the fastest turnaround that one could reasonably expect on that process is about six months. This might eventually be useful, but at the moment it seems like the model churn is huge and people insist you use this week's model for best results.
> The first generation HC1 chip is implemented in the 6 nanometer N6 process from TSMC. Each HC1 chip has 53 billion transistors on the package, most of it very likely for ROM and SRAM memory. The HC1 card burns about 200 watts, says Bajic, and a two-socket X86 server with ten HC1 cards in it runs 2,500 watts.
And what of that makes you assume that having a server with 10 HC1 cards is needed to run a single model version on that server?
So it lights money on fire extra fast, AI focused VCs are going to really love it then!!
This depends on how much better the models will get from now in, if Claude Opus 4.6 was transformed into one of these chips and ran at a hypothetical 17k tokens/second, I'm sure that would be astounding, this depends on how much better claude Opus 5 would be compared to the current generation
I’m pretty sure they’d need a small data center to run a model the size of Opus.
Alternatively, you could run far more RAG and thinking to integrate recent knowledge, I would imagine models designed for this putting less emphasis on world knowledge and more on agentic search.
Maybe; models with more embedded associations are also better at search. (Intuitively, this tracks; a model with no world knowledge has no awareness of synonyms or relations (a pure markov model), so the more knowledge a model has, the better it can search.) It’s not clear if it’s possible to build such a model, since there doesn’t seem to be a scaling cliff.
I'm thinking the best end result would come from custom-built models. An 8 billion parameter generalized model will run really quickly while not being particularly good at anything. But the same parameter count dedicated to parsing emails, RAG summarization, or some other specialized task could be more than good enough while also running at crazy speeds.
> What is a task that is extremely high value, only require a small model intelligence, require tremendous speed, is ok to run on a cloud due to power requirements, AND will be used for years without change since the model is etched into silicon?
Video game NPCs?
The company slogan is great: "The Model is The Computer"
It's an homage to Jensen: "The display is the computer"
The speed is ridiunkulous. No doubt.
The quantization looks pretty severe, which could make the comparison chart misleading. But I tried a trick question suggested by Claude and got nearly identical results in regular ollama and with the chatbot. And quantization to 3 or 4 bits still would not get you that HOLY CRAP WTF speed on other hardware!
This is a very impressive proof of concept. If they can deliver that medium-sized model they're talking about... if they can mass produce these... I notice you can't order one, so far.
I doubt many of us will be able to order one for a long while. There is a significant number of existing datacentre and enterprise use-cases that will pay a premium for this.
Additionally LLMs have been tested, found valuable in benchmarks, but not used for a large number of domains due to speed and cost limitations. These spaces will eat up these chips very quickly.
I tried the chatbot. jarring to see a large response come back instantly at over 15k tok/sec
I'll take one with a frontier model please, for my local coding and home ai needs..
Absolute insanity to see a coherent text block that takes at least 2 minutes to read generated in a fraction of a second. Crazy stuff...
Not at all if you consider the internet pre-LLM. That is the standard expectation when you load a website.
The slow word-by-word typing was what we started to get used to with LLMs.
If these techniques get widespread, we may grow accustomed to the "old" speed again where content loads ~instantly.
Imagine a content forest like Wikipedia instantly generated like a Minecraft word...
Yes, but the quality of the output leaves to be desired. I just asked about some sports history and got a mix of correct information and totally made up nonsense. Not unexpected for an 8k model, but raises the question of what the use case is for such small models.
8b models are great at converting unstructured data to a structured format. Say, you want to transcribe all your customer calls and get a list of issues they discussed most often. Currently with the larger models it takes me hours.
A chatbot which tells you various fun facts is not the only use case for LLMs. They're language models first and foremost, so they're good at language processing tasks (where they don't "hallucinate" as much).
Their ability to memorize various facts (with some "hallucinations") is an interesting side effect which is now abused to make them into "AI agents" and what not but they're just general-purpose language processing machines at their core.
You have a misunderstanding of what LLMs are good at.
Care to enlighten me?
Don't ask a small LLM about precise minutiae factual information.
Alternatively, ask yourself how plausible it sounds that all the facts in the world could be compressed into 8k parameters while remaining intact and fine-grained. If your answer is that it sounds pretty impossible... well it is.
I don't think he does. Larger models are definitely better at not hallucinating. Enough that they are good at answering questions on popular topics.
Smaller models, not so much.
Not sure if you're correct, as the market is betting trillions of dollars on these LLMs, hoping that they'll be close to what the OP had expected to happen in this case.
The market didn't throw trillions of dollars to develop Llama 3 8B.
What GP is expected to happen has happened around late 2024 ~ early 2025 when LLM frontends got web search feature. It's old tech now.
Reminds me of that solution to Fermi's paradox, that we don't detect signals from extraterrestrial civilizations because they run on a different clock speed.
Iain M Banks’ The Algebraist does a great job of covering that territory. If an organism had a lifespan of millions of years, they might perceive time and communication differently to say a house fly or us.
The speed of the chatbot's response is startling when you're used to the simulated fast typing of ChatGPT and others. But the Llama 3.1 8B model Taalas uses predictably results in incorrect answers, hallucinations, poor reliability as a chatbot.
What type of latency-sensitive applications are appropriate for a small-model, high-throughput solution like this? I presume this type of specialization is necessary for robotics, drones, or industrial automation. What else?
Coding, for some future definition of "small-model" that expands to include today's frontier models. What I commented a few days ago on codex-spark release:
"""
We're going to see a further bifurcation in inference use-cases in the next 12 months. I'm expecting this distinction to become prominent:
(A) Massively parallel (optimize for token/$)
(B) Serial low latency (optimize for token/s).
Users will switch between A and B depending on need.
Examples of (A):
- "Use subagents to search this 1M line codebase for DRY violations subject to $spec."
An example of (B):
- "Diagnose this one specific bug."
- "Apply these text edits".
(B) is used in funnels to unblock (A).
"""
You could build realtime API routing and orchestration systems that rely on high quality language understanding but need near-instant responses. Examples:
1. Intent based API gateways: convert natural language queries into structured API calls in real time (eg., "cancel my last order and refund it to the original payment method" -> authentication, order lookup, cancellation, refund API chain).
2. Of course, realtime voice chat.. kinda like you see in movies.
3. Security and fraud triage systems: parse logs without hardcoded regexes and issue alerts and full user reports in real time and decide which automated workflows to trigger.
4. Highly interactive what-if scenarios powered by natural language queries.
This effectively gives you database level speeds on top of natural language understanding.
I'm wondering how much the output quality of a small model could be boosted by taking multiple goes at it. Generate 20 answers and feed them back through with a "rank these responses" prompt. Or doing something like MCTS.
Isn't this what thinking models do internally? Chain of thoughts?
No. Chain of thought it just the model generating a single answer for longer inside <think></think> tags which are not shown in the final response. The strategy of generating different answers in parallel is something different (which can be used in conjunction with chain of thought) and is the thing used by models like Gemini 3 Deep Think and GPT-5.2 Pro.
Routing in agent pipelines is another use. "Does user prompt A make sense with document type A?" If yes, continue, if no, escalate. That sort of thing
Maybe summarization? I’d still worry about accuracy but smaller models do quite well.
Wow I'm impressed. I didn't actually think we'd see it encoded on chips. Or well I knew some layer of it could be, some sort of instruction set and chip design but this is pretty staggering. It opens the door to a lot of things. Basically it totally destroys the boundaries of where software will go but I also think we'll continue to see some generic chips show up that hit this performance soon enough. But the specialised chips with encoded models. This could be what ends up in specific places like cars, planes, robots, etc where latency matters. Maybe I'm out of the loop, I'm sure others and doing it including Google.
ASIC inference is clearly the future just as ASIC bitcoin mining was
Asking it what its knowledge cut-off is interesting, it doesn't seem to be consistent even within a single response. Sometimes it responds to say it cuts off 2020 too.
You
What is your knowledge cut-off?
Jimmy
My knowledge cut-off is 2022, which means that my training data is current up to 2021, but
I have been trained on a dataset that is updated periodically. If you have any specific
questions about events or topics that occurred after 2021, I may not have information on
those topics. However, I can still provide general information and context on those topics
to help guide further research.
Sounds like they aren’t providing a system prompt which tells it what its knowledge cutoff is. It doesn’t know unless it’s told. Given the limited context, it makes sense they wouldn’t use up valuable space for that.
>Founded 2.5 years ago, Taalas developed a platform for transforming any AI model into custom silicon. From the moment a previously unseen model is received, it can be realized in hardware in only two months.
So this is very cool. Though I'm not sure how the economics work out? 2 months is a long time in the model space. Although for many tasks, the models are now "good enough", especially when you put them in a "keep trying until it works" loop and run them at high inference speed.
Seems like a chip would only be good for a few months though, they'd have to be upgrading them on a regular basis.
Unless model growth plateaus, or we exceed "good enough" for the relevant tasks, or both. The latter part seems quite likely, at least for certain types of work.
On that note I've shifted my focus from "best model" to "fastest/cheapest model that can do the job". For example testing Gemini Flash against Gemini Pro for simple tasks, they both complete the task fine, but Flash does it 3x cheaper and 3x faster. (Also had good results with Grok Fast in that category of bite-sized "realtime" workflows.)
This would be killer for exploring simultaneous thinking paths and council-style decision taking. Even with Qwen3-Coder-Next 80B if you could achieve a 10x speed, I'd buy one of those today. Can't wait to see if this is still possible with larger models than 8B.
It uses 10 chips for 8B model. It’d need 80 chips for an 80b model.
Each chip is the size of an H100.
So 80 H100 to run at this speed. Can’t change the model after you manufacture the chips since it’s etched into silicon.
As many others in this conversation have asked, can we have some sources on the idea that the model is spread across chips? You keep making the claim, but no one (myself included) else has any idea where that information comes from or if it is correct.
I was indeed wrong about 10 chips. I thought they would use llama 8B 16bit and a few thousand context size. It turns out, they used llama 8B 3bit with only 1k context size. That made me assume they must have chained multiple chips together since the max SRAM on TSMC n6 for reticle sized chip is only around 3GB.
I'm sure there is plenty of optimization paths left for them if they're a startup. And imho smaller models will keep getting better. And a great business model for people having to buy your chips for each new LLM release :)
One more thing. It seems like this is a Q3 quant. So only 3GB RAM requirement.
10 H100 chips for 3GB model.
I think it’s a niche of a niche at this point.
I’m not sure what optimization they can do since a transistor is a transistor.
Do we know that it needs 10 chips to run the model? Or are the servers for the API and chatbot just specced with 10 boards to distribute user load?
If you etch the bits into silicon, you then have to accommodate the bits by physical area, which is the transistor density for whatever modern process they use. This will give you a lower bound for the size of the wafers.
This is what’s gonna be in the brain of the robot that ends the world.
The sheer speed of how fast this thing can “think” is insanity.
I wonder if this makes the frontier labs abandon the SAAS per-token pricing concept for their newest models, and we'll be seeing non-open-but-on-chip-only models instead, sold by the chip and not by the token.
It could give a boost to the industry of electron microscopy analysis as the frontier model creators could be interested in extracting the weights of their competitors.
The high speed of model evolution has interesting consequences on how often batches and masks are cycled. Probably we'll see some pressure on chip manufacturers to create masks more quickly, which can lead to faster hardware cycles. Probably with some compromises, i.e. all of the util stuff around the chip would be static, only the weights part would change. They might in fact pre-make masks that only have the weights missing, for even faster iteration speed.
This is an interesting piece of hardware though when they go multi-chip for larger models the speed will no doubt suffer.
They'll also be severely limited on context length as it needs to sit in SRAM. Looks like the current one tops out at 6144 tokens which I presume is a whole chips worth. You'd also have to dedicate a chip to a whole user as there's likely only enough SRAM for one user's worth of context. I wonder how much time it takes them to swap users in/out? I wouldn't be surprised if this chip is severely underutilized (can't use it all when running decode as you have to run token by token with one users and then idle time as you swap users in/out).
Maybe a more realistic deployment would have chips for linear layers and chips for attention? You could batch users through the shared weight chips and then provision more or less attention chips as you want which would be per user (or shared amongst a small group 2-4 users).
If it's not reprogrammable, it's just expensive glass.
If you etch the bits into silicon, you then have to accommodate the bits by physical area, which is the transistor density for whatever modern process they use. This will give you a lower bound for the size of the wafers.
This can give huge wafers for a very set model which is old by the time it is finalized.
Etching generic functions used in ML and common fused kernels would seem much more viable as they could be used as building blocks.
Reading the in depth article also linked in this thread, they say that only 2 layers need to change most of the time. They claim from new model to PCB in 2 months. Let's see, but sounds promising.
Models don’t get old as fast as they used to. A lot of the improvements seem to go into making the models more efficient, or the infrastructure around the models. If newer models mainly compete on efficiency it means you can run older models for longer on more efficient hardware while staying competitive.
If power costs are significantly lower, they can pay for themselves by the time they are outdated. It also means you can run more instances of a model in one datacenter, and that seems to be a big challenge these days: simply building an enough data centres and getting power to them. (See the ridiculous plans for building data centres in space)
A huge part of the cost with making chips is the masks. The transistor masks are expensive. Metal masks less so.
I figure they will eventually freeze the transistor layer and use metal masks to reconfigure the chips when the new models come out. That should further lower costs.
I don’t really know if this makes sanse. Depends on whether we get new breakthroughs in LLM architecture or not. It’s a gamble essentially. But honestly, so is buying nvidia blackwell chips for inference. I could see them getting uneconomical very quickly if any of the alternative inference optimised hardware pans out
This is really cool! I am trying to find a way to accelerate LLM inference for PII detection purposes, where speed is really necessary as we want to process millions of log lines per minute, I am wondering how fast we could get e.g. llama 3.1 to run on a conventional NVIDIA card? 10k tokens per second would be fantastic but even at 1k this would be very useful.
PII redaction is a really good use-case.
Also, "10k tokens per second would be fantastic" might not be sufficient (even remotely) if you want to "process millions of log lines per minute".
Assuming a single log line at just 100 tokens, you need (100 * 2 million / 60) ~ 3.3 million tokens per second processing speed :)
Gemini Flash 2.5 lite does 400 tokens/sec. Is there benefit to going faster than a person can read?
Agents also "read", so yes there is. Think about spinning up 10, 20, 100 sub agents for a small task and they all return near instant. That's the usecase, not the chatbot.
There are so many use cases for small and super fast models that are already in size capacity -
* Many top quality tts and stt models
* Image recognition, object tracking
* speculative decoding, attached to a much bigger model (big/small architecture?)
* agentic loop trying 20 different approaches / algorithms, and then picking the best one
* edited to add! Put 50 such small models to create a SOTA super fast model
If they made a low power/mobile version, this could be really huge for embedded electronics. Mass produced, highly efficient "good enough" but still sort of dumb ais could put intelligence in house hold devices like toasters, light switches, and toilets. Truly we could be entering into the golden age of curses.
Oh god, this is the new version of every device having Bluetooth and an app and being called “smart”.
I just wanted some toast, but here I am installing an app, dismissing 10 popups, and maybe now arguing with a chat bot about how I don’t in fact want to turn on notifications.
I think the thing that makes 8b sized models interesting is the ability to train unique custom domain knowledge intelligence and this is the opposite of that. Like if you could deploy any 8b sized model on it and be this fast that would be super interesting, but being stuck with llama3 8b isn't that interesting.
The "small model with unique custom domain knowledge" approach has a very low capability ceiling.
Model intelligence is, in many ways, a function of model size. A small model tuned for a given domain is still crippled by being small.
Some things don't benefit from general intelligence much. Sometimes a dumb narrow specialist really is all you need for your tasks. But building that small specialized model isn't easy or cheap.
Engineering isn't free, models tend to grow obsolete as the price/capability frontier advances, and AI specialists are less of a commodity than AI inference is. I'm inclined to bet against approaches like this on a principle.
But as models are changing rapidly and new architectures coming up, how do they scale and also we do t yet know the current transformer architecture will scale more than it already is. Soo many ope questions but VCs seems to be pouring money.
I was all praise for Cerberus, and now this ! $30 M for PCIe card in hand, really makes it approachable for many startups.
try here, I hate llms but this is crazy fast. https://chatjimmy.ai/
"447 / 6144 tokens"
"Generated in 0.026s • 15,718 tok/s"
We need that for this chinese 3B model that think 45s for hello world but also solves math.
The full answer pops in milliseconds, it's impressive and feels like a completely different technology just by foregoing the need to stream the output.
Because most models today generate slowish, they give the impression of someone typing on the other end. This is just <enter> -> wall of text. Wild
I am super happy to see people working on hardware for local llm. Yet, isnt it premature ? Space is still evolving. Today, i refuse to buy a gpu because i do not know what will be the best model tomorrow. Waiting to get a on the shelf device to run an opus like model
This is pretty wild! Only Llama3.1-8B, but this is only their first release so you can assume they're working on larger versions.
So what's the use case for an extremely fast small model? Structuring vast amounts of unstructured data, maybe? Put it in a little service droid so it doesn't need the cloud?
I totally buy the thesis on specialization here, I think it makes total sense.
Asides from the obvious concern that this is a tiny 8B model, I'm also a bit skeptical of the power draw. 2.4 kW feels a little bit high, but someone else should try doing the napkin math compared to the total throughput to power ratio on the H200 and other chips.
wonder if at some point you could swap the model as if you were replacing a cpu in your pc or inserting a game cartridge
"Many believe AI is the real deal. In narrow domains, it already surpasses human performance. Used well, it is an unprecedented amplifier of human ingenuity and productivity."
Sounds like people drinking the Kool-Aid now.
I don't reject that AI has use cases. But I do reject that it is promoted as "unprecedented amplifier" of human xyz anything. These folks would even claim how AI improves human creativity. Well, has this been the case?
For me, this is entirely true.
I'm progressing with my side projects like I've never before.
Same, I would have given up on them long ago, I no longer code at all now. Why would I when the latest models can do it better, faster and without the human limitations of tiredness, emotional impacts etc.
> These folks would even claim how AI improves human creativity. Well, has this been the case?
Yes. Example: If you've never programmed in language X, but want to build something in it, you can focus on getting from 0 to 1 instead of being bogged down in the idiosyncrasies of said language.
Strange that they apparently raised $169M (really?) and the website looks like this. Don't get me wrong: Plain HTML would do if "perfect", or you would expect something heavily designed. But script-kiddie vibe coded seems off.
The idea is good though and could work.
Strange that they raised money at all with an idea like this.
It's a bad idea that can't work well. Not while the field is advancing the way it is.
Manufacturing silicon is a long pipeline - and in the world of AI, one year of capability gap isn't something you can afford. You build a SOTA model into your chips, and by the time you get those chips, it's outperformed at its tasks by open weights models half their size.
Now, if AI advances somehow ground to a screeching halt, with model upgrades coming out every 4 years, not every 4 months? Maybe it'll be viable. As is, it's a waste of silicon.
Poverty of imagination here, plenty uses of this and its a prototype at this stage.
What uses, exactly?
The prototype is: silicon with a Llama 3.1 8B etched into it. Today's 4B models already outperform it.
Token rate in five digits is a major technical flex, but, does anyone really need to run a very dumb model at this speed?
The only things that come to mind that could reap a benefit are: asymmetric exotics like VLA action policies and voice stages for V2V models. Both of which are "small fast low latency model backed by a large smart model", and both depend on model to model comms, which this doesn't demonstrate.
In a way, it's an I/O accelerator rather than an inference engine. At best.
Even if this first generation is not useful, the learning and architecture decisions in this generation will be. You really can't think of any value to having a chip which can run LLMs at high speed and locally for 1/10 of the energy budget and (presumably) significantly lower cost than a GPU?
If you look at any development in computing, ASICs are the next step. It seems almost inevitable. Yes, it will always trail behind state of the art. But value will come quickly in a few generations.
With LLMs this fast, you could imagine using them as any old function in programs.
maybe they're betting on improvement in models to plateau, and that having a fairly stablized capable model that is orders of magnitude faster than running on GPU's can be valuable in the future?
I don't know why, but my ultra wide monitor absolutely hates that site. The whole screen is flickering trying to deal with the annoying background. Thank the gods for reader mode.
Amazing speed. Imagine if its standardised like the GPU card equivalent in the future.
New models come out, time to upgrade your AI card, etc.
This is impressive. If you can scale it to larger models, and somehow make the ROM writeable, wow, you win the game.
So I'm guessing this is some kind of weights as ROM type of thing? At least that's how I interpret the product page, or maybe even a sort of ROM type thing that you can only access by doing matrix multiplies.
You shouldn't need any ROM. It's likely the architecture is just fixed hardware with weights loaded in via scan flip-flows. If it was me making it, I'd just design a systolic array. Just multipliers feeding into multipliers, without even going through RAM.
Can it scale to an 800 billion param model? 8B parameter models are too far behind the frontier to be useful to me for SWE work.
Or is that the catch? Either way I am sure there will be some niche uses for it.
That seems promising for applications that require raw speed. Wonder how much they can scale it up - 8B model quantized is very usable but still quite small compared to even bottom end cloud models.
Wow. I’m finding it hard to even conceive of what it’d be like to have one of the frontier models on hardware at this speed.
Fast but the output is shit due to the contrained model they used. Doubt we'll ever get something like this for the large Param decent models.
The token throughput improvements are impressive. This has direct implications for usage-based billing in AI products — faster inference means lower cost per request, which changes the economics of credits-based pricing models significantly.
Jarring to see these other comments so blindly positive.
Show me something at a model size 80GB+ or this feels like "positive results in mice"
There are a lot of problems solved by tiny models. The huge ones are fun for large programming tasks, exploration, analysis, etc. but there's a massive amount of processing <10GB happening every day. Including on portable devices.
This is great even if it can't ever run Opus. Many people will be extremely happy about something like Phi accessible at lightning speed.
Positive results in mice also known as being a promising proof of concept. At this point, anything which deflates the enormous bubble around GPUs, memory, etc, is a welcome remedy. A decent amount of efficient, "good enough" AI will change the market very considerably, adding a segment for people who don't need frontier models. I'd be surprised if they didn't end up releasing something a lot bigger than they have.
Reminds me of when bitcoin started running on ASICs. This will always lag behind the state of the art, but incredibly fast, (presumably) power efficient LLMs will be great to see. I sincerely hope they opt for a path of selling products rather than cloud services in the long run, though.
I still believe this is the right - and inevitable - path for AI, especially as I use more premium AI tooling and evaluate its utility (I’m still a societal doomer on it, but even I gotta admit its coding abilities are incredible to behold, albeit lacking in quality).
Everyone in Capital wants the perpetual rent-extraction model of API calls and subscription fees, which makes sense given how well it worked in the SaaS boom. However, as Taalas points out, new innovations often scale in consumption closer to the point of service rather than monopolized centers, and I expect AI to be no different. When it’s being used sparsely for odd prompts or agentically to produce larger outputs, having local (or near-local) inferencing is the inevitable end goal: if a model like Qwen or Llama can output something similar to Opus or Codex running on an affordable accelerator at home or in the office server, then why bother with the subscription fees or API bills? That compounds when technical folks (hi!) point out that any process done agentically can instead just be output as software for infinite repetition in lieu of subscriptions and maintained indefinitely by existing technical talent and the same accelerator you bought with CapEx, rather than a fleet of pricey AI seats with OpEx.
The big push seems to be building processes dependent upon recurring revenue streams, but I’m gradually seeing more and more folks work the slop machines for the output they want and then put it away or cancel their sub. I think Taalas - conceptually, anyway - is on to something.
This is like microcontrollers, but for AI? Awesome! I want one for my electric guitar; and please add an AI TTS module...
> Though society seems poised to build a dystopian future defined by data centers and adjacent power plants, history hints at a different direction. Past technological revolutions often started with grotesque prototypes, only to be eclipsed by breakthroughs yielding more practical outcomes.
…for a privileged minority, yes, and to the detriment of billions of people whose names the history books conveniently forget. AI, like past technological revolutions, is a force multiplier for both productivity and exploitation.
What would it take to put Opus on a chip? Can it be done? What’s the minimum size?
I'm loving summarization of articles using their chatbot! Wow!
Does anyone have an idea how much such a component costs?
one step closer to being able to purchase a box of llms on aliexpress, though 1.7ktok/s would be quite enough
Is this hardware for sale ? The site doesn't say.
I wonder if this is the first step towards AI as an appliance rather than a subscription?
It was so fast that I didn't realise it had sent its response. Damn.
Hurrah, its dumb answer to the now classic "the car wash is 100m away, should I drive or walk?" appeared very quickly.
It's an 8B parameter model from a good while ago, what were your expectations?
For fun I'm imagining a future where you would be able to buy an ASIC with like an hard-wired 1B LLM model in it for cents and it could be used everywhere.
Jesus, it just generated a story in 0.039s.
Whoever doesn’t buy/replicate this in the next year is dead. Imagine OpenAI trying to sell you a platform that takes 15 minutes, when someone else can do it in 0.001s.
> generated a story
Was the story any good? The model used isn't comparable to something OpenAI would offer you today. What good is a fast gibberish generator?
Question:
> Generate a fiction book about an old sorcerer who, like all elderly magicians, has to go back to studying magic for two years at a magic school in order to be granted the right to keep his wand and to keep doing magic at his home.
Answer, generated in 0,063s @ 15 742 tok/s:
> The Relearning of Eryndor Thorne
> In the quaint village of Brindlemark, where ancient traditions and mystical energies coexisted, Eryndor Thorne had lived a life of quiet reverence. For nigh on five decades, he had wielded the arcane arts with precision and mastery, earning him the respect of his peers and the awe of the villagers. His home, a humble abode atop a hill overlooking the rolling hills and verdant valleys, was a sanctuary of sorts, a place where the very fabric of magic was woven into the air. His library was a treasure trove of ancient tomes, texts, and scrolls, filled with the wisdom of the ages. Yet, despite his mastery over the arcane, Eryndor felt the stirrings of a nagging concern. The once-renewable license to practice magic, a privilege granted by the Council of Mages, was on the cusp of expiration.
> As all elderly mages knew, every two decades, the Council would demand that those who had spent a lifetime mastering the arcane arts return to the prestigious Wysteria Academy, the hallowed halls of magic learning, to relearn the foundational principles of magic. This was a ritual as old as the craft itself, a test of the elderly mage's understanding, to prove they still possessed the fundamental knowledge and skills required to practice magic with precision and safety.
> Eryndor, like many of his fellow mages, had grown complacent in his mastery. He had mastered the intricacies of elemental magic, bending the winds, waters, and flames to his will. His wand, a family heirloom passed down through generations, had become an extension of his arm, channeling the power of magic with ease. Yet, despite his proficiency, the Council's decree was clear: Eryndor must return to Wysteria Academy for two years of reeducation.
> The journey was not an easy one. Packing his worn leather satchel with the few books and scrolls he deemed essential, Eryndor bid farewell to his loved ones in Brindlemark, leaving behind the familiarity of his home to embark on a journey that would take him to a place both wondrous and treacherous. At Wysteria Academy, he joined a cohort of students from all corners of the realm, each as skilled and eager as he once was. The relearning process was a grueling one, as if facing a mountain he had once conquered. New techniques were presented, new theories to unravel, and new expectations to meet. Eryndor, once a master, now sat at the feet of his teachers, absorbing the new knowledge like a parched traveler at an oasis.
> Among his fellow students, there was the young, fire-kissed mage, Elara, who wielded magic with an intensity that bordered on reckless abandon. Her fiery nature and quick wit often put her at odds with the strict, ancient traditions, earning her a certain notoriety among the academy's elder mages. Then there was the enigmatic, shadow-drawn Kael, whose mastery of the arcane was matched only by his mystery. Kael's affinity for the dark arts raised more than a few eyebrows among the faculty, but Eryndor, having once walked the fine line between light and shadow, saw something of himself in the young mage.
> As the years passed, Eryndor grew to appreciate the challenges and opportunities that came with his return to the academy. He found himself grappling with the nuances of magic anew, rekindling memories of his early days as a novice. The relearning process was as much about rediscovering himself as it was about mastering the arcane. His studies were a journey of self-discovery, one that tested the mettle of his will and the depths of his understanding.
> Upon completion of his studies, Eryndor stood before the Council once more, his wand in hand, his heart afire with the thrill of rediscovery. The Council's examination was not merely a test of his knowledge but a test of his character. Eryndor, like many of his peers, had grown complacent, but the rigors of relearning had rekindled a spark within him, a flame that would guide him through the trials ahead.
> With his renewed license granted, Eryndor returned to Brindlemark, his home and his heart rejuvenated. His library, once a testament to his mastery, was now a canvas for the new knowledge he had acquired. His wand, now an extension of his rekindled passion for the arcane, channelled magic with a precision and flair that was unmatched. Eryndor Thorne, the elderly mage, had not only relearned magic but had rediscovered himself, a testament to the transformative power of education and the enduring spirit of the arcane.
this is absolutely mindblowing speed. imagine this with opus or 5.2
Wow, this is great.
To the authors: do not self-deprecate your work. It is true this is not a frontier model (anymore) but the tech you've built is truly impressive. Very few hardware startups have a v1 as good as this one!
Also, for many tasks I can think of, you don't really need the best of the best of the best, cheap and instant inference is a major selling point in itself.
It's crazily fast. But 8B model is pretty much useless.
Anyway VCs will dump money onto them, and we'll see if the approach can scale to bigger models soon.
Impressive stuff.
17k token/sec is $0.18/chip/hr for the size of H100 chip if they want to compete with the market rate[1]. But 17k token/sec could lead to some new usecases.
[1]: https://artificialanalysis.ai/models/llama-3-1-instruct-8b/p...
Holy Hallucinations, Batman...
User You: What is the airspeed of an unladen swallow/
Jimmy:
A reference to the classic line from Monty Python and the Holy Grail!
Thus far it is correct but here it comes:
In the movie, a group of medieval scholars, the Knights who say "Ni!" (also known as the Bridgekeepers), ask a king's servant, a blacksmith, if an unladen swallow makes good time. The blacksmith, played by John Cleese, responds with the now-famous line:
"And what's the airspeed velocity of an unladen swallow?"
To which the blacksmith responds:
"What do you mean? An unladen swallow?"
The blacksmith then explains that an unladen swallow is when a swallow (a type of bird) is flying in the air without carrying any burden, implying that it is moving at a speed that is not affected by carrying any weight or load.
Nope, that is not at all how it went. No blacksmiths, not related to the Knights who say 'Ni!' - who have nothing scholarly about them, they wanted a shrubbery and demanded another one when their wish was fulfilled. It was the Bridgekeeper - not related to the aforementioned knights - who asked 3 questions and would throw anyone who could not answer all three into the chasm. When he asked Arthur - played by Cleese - the question about the airspeed of the swallow he asked the Bridgekeeper whether he meant an African or European swallow. The Bridgekeeper did not have an answer and was thrown into the chasm, problem solved.
However, in reality, swallows are not typically "unladen" in flight. They are small birds that fly at relatively fast speeds, usually around 10-15 km/h (6-9 mph), but they are not usually carrying any burdens!
Needless LLM-blabber.
The "airspeed velocity of an unladen swallow" has become a meme and a cultural reference point, often used humorously or ironically to refer to situations where someone is trying to make an absurd or non-sensical argument or ask an absurd question.
Somewhat correct but not necessary in this context.
The correct answer to the question would have been Do you mean an African or European swallow? followed by a short reference to the movie.
Is it already available to buy, or is this a “pay now, get it later” kind of new ASIC miner? Sorry for being skeptical, but AI is the new "crypto coin", and the crypto bros are still around.
[dead]
Scale this then close the loop and have fabs spit out new chips with latest weights every week that get placed in a server using a robot, how long before AGI?
The article doesn't say anything about the price (it will be expensive), but it doesn't look like something that the average developer would purchase.
An LLM's effective lifespan is a few months (ie the amount of time it is considered top-tier), it wouldn't make sense for a user to purchase something that would be superseded in a couple of months.
An LLM hosting service however, where it would operate 24/7, would be able to make up for the investment.
I tried the trick question I saw here before, about the make 1000 with 9 8s and additions only
I know it's not a resonating model, but I keep pushing it and eventually it gave me this as part of it's output
888 + 88 + 88 + 8 + 8 = 1060, too high... 8888 + 8 = 10000, too high... 888 + 8 + 8 +ประก 8 = 1000,ประก
I googled the strange symbol, it seems to mean Set in thai?
I don't think it's very valuable to talk about the model here, the model is just an old Llama. It's the hardware that matters.