AMD acquires Taalas to boost inference performance by etching models in silicon

I'm surprised neither OpenAI nor Anthropic made this move first. The Chinese open weight models are pulling ahead and commoditizing their value proposition.Baking models onto silicon would've been the next logical move to get a moat.Google is already doing this and has an experimental project on top of already having TPUs and cramming their quantized flash onto individual TPUs for inference. reply: Personally I think Apple should have acquired them. if you could burn a gemma4 class model into an iphone and actually get extremely low latency and low battery usage it would feel like the future IMO. even if it means you wont get frontier intelligence, there might actually be incentive to buy a new mobile device every year again.

I've been eagerly awaiting their 2nd gen HC2, which uses multiple chips to host a "mid sized reasoning" [1] model. Its due in summer according to the article, I wonder if it will ever be released in that form now.[1] https://www.forbes.com/sites/karlfreund/2026/02/19/taalas-la...

Thinking that five or six years from now, Fable-level intelligence could be provided at 100x the current speed... makes me feel lost. I cannot imagine what the future will look like. reply: Cerebras already runs large models like Kimi 2.6 or GLM at like 30x speed. 100 times is next year, not six years.You can actually test it out on their website, just imagine 3 x faster and maybe 15% smarter.

I would've hoped the company stayed independent instead of being engulfed into a behemoth. I'd like to see more diversity in the hardware ecosystem, but I guess the economics of hardware manufacturing aren't there.

What I like about this, is that it significantly increases the probability of a sci-fi scenario where you're picking up a hot chip on the black market; rumor has it, Mythos 9 weights baked in...

Waiting for intelligence on a stick, plugin an USB, characters in, characters out.100% local and no leaks.

Question: we currently emulate neural networks by performing matrix math in synchronous clock CPU architectures. Would it not be better to abandon synchronization and etch neuron synapses directly in silicon, keeping only the weights variable? I think some researchers are pursuing this, but I forget what the approach is called.

Given the fast churn of the models, how does it work out?Won’t the silicon etched model already be 1 or more versions behind by the time the silicon comes out.Though if it’s cheap enough, there certainly can be a market for cheaper model inferences. reply: This is a feature for most local use cases. You don't want all your work flows to start failing because of a model update.

This is neat but IMO a little crazy.Something I personally haven’t seen much of, in all the discussions of model benchmarks and AI breakthroughs, is a distinction between “peak performance” and “reliable performance”. The “peak performance” of frontier models is very high: they’re solving open math problems, analyzing large codebases, etc. But my subjective impression is that “reliable performance” is mid at best: out of 100 random questions I might think to ask, it’s likely to say something wrong or stupid a handful of times at least.I think there’s inherent tension between the two: the more a model reaches or outright hallucinates, the more likely it is to come up with tricky, subtle solutions to problems (I think people are somewhat like this too: Terry Tao’s brother is nonverbal, Jim Watson’s son has severe schizophrenia, etc). But then the less likely it is to generate a sensible email reply.I use models all the time for coding, but I would not let one take over my daily correspondence....

I've been showing people chatjimmy for months - it's incredible. Both reasoning and tool use generation scale with TPS. Imagine 100x more reasoning on a model, or 100x parallel tool uses.

This is probably a win-win. The team gets paid, and we get greater assurance that their best ideas and architectures -- which are truly impressive -- are going to see the light of day in actual products.

Well so much for that dream.Guess we can look forward to picking these up ex-enterprise on ebay for under $5k a pop in a decade or two

AMD could have saved their money and used their own hardware! I've got a language model doing 60k tok/s on AMD hardware already, a Xilinx Kria K26 SOM, with the weights baked into URAM/BRAM with zero DRAM in the token loop. Same thesis as Taalas: single-stream decode is bandwidth bound, so stop fetching weights from far away.Caveats stacked high, obviously. It's 3.16M parameters (tinystories, and I also have a kevin-speak lemmatised version), the tokens are characters, and the 60k record is 16 streams that each remember exactly one token of context, so it's blisteringly fast at saying nothing. The honest build with full context and KV caching still does ~19k tok/s on one stream though.I keep messing with the blogpost with the live demo, but I'm planning on flipping it to live in the next day or two

Really hoped to see their hw out in the wild one day

What does it take to go from here to a model on a pcie card or an m.2 card, so I can plug one into my workstation / laptop? Will 'intelligence' become much like a gpu, where most people just live with the performance of whatever they have installed, outside large companies that must have cutting edge, or prosumers that have a incrementally better version than the masses?Are we a couple years away, a decade away, or something else?

I think the big news is that AMD is getting into memory-business so they won't be so dependent on Hynix and what have you. Memory is the bottleneck currently.

It would be quite ironic if this technology would render all those AI data centers practically useless. If the next step are just a much smaller amount of expensive chips, and the bottleneck becomes manufacturing those chips fast. Not building huge data centers and fighting for electrical power.

Is there any LLM from exactly one year ago that would be worth running?In Aug 2025 you had- OpenAI o3- Opus 4.1- Gemini 2.5 Pro- Grok 4Even if those were almost free to run, you'd be way better off with Deepseek flash 0731 or GPT 5.6 Luna, which already are almost free.Other than for things where the t/s are critical, it seems like a bad idea to etch a model into silicon.

While this design is self-limiting I think its a good approach. It doesn't take an entirely new architecture or infinite memory to produce significant performance improvement.

People are missing the point if they think this is useless because frontier models keep changing every few months.We really, really need better secondary models that can do things fast and do them cheaply for lots of dumb tasks. Not only because it can be used as sub agents by frontier models, but also because it can be like a universal grease for all kinds of software.I've got an app I am building and I don't want to tie myself with frontier models because I'll never be able to beat openai/anthropic. I just want a simple, cheap, instantaneous model that can just go through my documentation and tell the user what to do next and how to integrate with whatever ai subscription they have.

Can anyone comment on the economics and likely turnaround times of this process, when it’s more mature?Would it be realistic for a frontier lab to deploy this or would the turnaround time mean the model is always too out of date?Assuming the weights and architecture are eventually stable, how much cheaper would this end up being?

Honestly, this is starting to make more and more sense. SOTA models are starting to converge to certain architecture and capabilities. I wouldn’t be surprised we end up with a base model ASIC + “fine tune” card where it’s a physical LoRA style adapter.

See also: Twitter statement from Taalas https://x.com/taalas_inc/status/2085458427757937097

It obviously won’t be continuous delivery but could make sense if the lifecycle of a model (train, deploy, iterate (meaningfully) is about 1-2 years. In that case it fits nicely in the “this year’s model” already established with cars, phones, etc.

Field reprogrammable, it's an FPGA on steroids. Field upgradable.Burnt in, it needs a zif socket and easy access in every car, aircraft, a pull out slot in a phone, or it's new era planned obselescence.

I’ve been using Gemma as my default (via Kagi) because it’s served on Cerebas hardware. The speed is honestly a game changer for day to day queries.

So, Kimi K3 in silicon sometime soon?

How's that jive with the fact that they're introducing a new model every other week?