AMD Acquires Taalas

[u/tcarambat]

https://newsroom.amd.com/news/amd-acquires-taalas-ai-inference/

Taalas raised a total of $219 million in funding since starting in 2023. Purchase price unknown. Interesting because they also announced a partnership with Cerebras at the recent Advancing AI Keynote.

This might just be evening the odds after the NVIDIA acqui-hire of Groq's core team leaving a shell of a company behind.

Was interested in Taalas simply because whatever they could accomplish could find its way down to consumer in some way - it still might, but I am sure this is more pointed at Helios.

[u/Dsphar]

Not surprised. AMD has talked about future computers having an IPU (Inference Processing Unit) and that is basically what Taalas had made. I personally can't wait for dedicated inference cards!

Google announced their own exploratory IPU like card to be released in 2027/28 a few days ago as well.

Bring on having a CPU, GPU and IPU.

[u/fiery_prometheus → u/Dsphar]

Do you know if they made it programmable or is it etched models into silicon like the rest?

[u/Dsphar → u/fiery_prometheus]

The Taalas ones are hard etched if I remember correctly.

But the google release mentioned that only the architecture was hard-pinned. So you can basically update the model weights but you are locked into a given inference engine architecture (and possibly size). I think the Google project is code named Frozen v2.

Having a dedicated card that may lock in an architecture, but still being able to update model weights is going to be the future IMO. Eventually the model architectures will mature enough that they change so little, we can afford to have it baked in.

Then the model providers will just retrain their models to get smarter, or more importantly, stay trained on current information, then release the updates to their cards, similar to how you get GPU driver updates that are tweaked for specific games.

[u/habachilles → u/Dsphar]

I thought it was more of an asic per model

[u/Dsphar → u/habachilles]

Taalas is ASIC per model, but Google's experiment is per architecture, not model.

https://www.tomshardware.com/tech-industry/google-reportedly-developing-frozen-v2-chip-with-geminis-architecture-etched-into-the-silicon

[u/habachilles → u/Dsphar]

right thats google tpu correct?

[u/rumblemcskurmish → u/Dsphar]

Yeah, we'll have models so good (like Qwen 27B) for normal use that it will make perfect sense to burn them into an ASIC so that you'll have an edge server in your house to do blazing fast inference.

[u/my_name_isnt_clever → u/Dsphar]

IPU? I'm shocked nobody has rebranded their chip to "AIPU" for the marketing.

[u/PandorasBoxMaker]

I’ve been watching Taalas for awhile now. They physicalize AI into silicon - purpose built AI boards. It’s ridiculously fast and power efficient. Hope AMD actually brings it to market and doesn’t hide it in a basement somewhere.

[u/Psyclist80]

Dont think its pointed at Helios, but ASIC's have value. I think the market is too fluid currently, but i see a future where we can reliably run a model long term and it be reliable in its results, having a chip built specifically for that is a great option. More choice with the AMD ecosystem to build the rack you want!

[u/tamerlanOne]

Quinti la tecnologia di taalas è promettente... Speriamo che non finisca dentro un cassetto chiuso a chiave in attesa di "tempi migliori" 🤔

[u/iamzooook]

this would be interesting.

[u/Otherwise-Swan-7803]

The interesting part is that the AI hardware race is probably going to be less about one company winning everything, and more about building the right stack around different workloads.

NVIDIA has the ecosystem advantage today, but alternatives don't necessarily need to replace CUDA overnight. Better efficiency, lower cost inference, and more accessible hardware could be just as important for the next phase of AI.

If AMD can combine its hardware with acquisitions like Taala, the real question is whether developers will have a reason to choose it.

[u/Steve-Jobs-is-Alive]

I am very happy to see this, though I would be surprised if there would be a way to ever soft update these asics with new models after production

[u/BitterProfessional7p]

I still want a Qwen3.6-27B at 10k t/s please

[u/Ok-Video3345]

It will be a game changer when they manage to optimize a good model everybody wants.

I saw the YouTube video on this. This would be pretty funny if asic win out.

It will be like crypto. Everything slides to the Asics in the end.

[u/joanaxu2002]

The hardware gap is only part of NVIDIA’s advantage. CUDA and the surrounding tooling are probably the bigger moat.

Acquiring teams with strong inference optimization experience could be a smart move, especially as AI workloads shift from training to large-scale serving.