The specs look impressive. It is always good to have competition.
They announced tapeout in October with planned dev boards next year. Vaporware is when things don’t appear, not when they are on their way (it takes some time for hardware).
It’s also strategically important for Europe to have its own supply. The current and last US administration have both threatened to limit supply of AI chips to European countries, and China would do the same (as they have shown with Nexperia).
And of course you need the software stack with it. They will have thought of that.
https://vsora.com/vsora-announces-tape-out-of-game-changing-...
These kinds of things-- cheaper-than-NVIDIA cards that can produce a lot of tokens or run large models cheaply are absolutely necessary to scale text models economically.
Without things like these-- those Euclyd things, those Groq things, etc. no one will be able to offer up big models at prices where people will actually use them, so lack of things like this actually cripples training of big models too.
If the price/token graph is right, this would mean 2.5x more tokens, which presumably means actually using multiple prompts to refine something before producing the output, or to otherwise produce really long non-output sequences during the preparation the output. This also fits really well with the Chinese progress in LLM RL for maths. I suspect all that stuff is totally general and can be applied to non-maths things too.
Multiple independent sources confirmed the tape-out: EE Times: https://www.eetimes.eu/vsora-tapes-out-ai-inference-chip-for...
L’Informaticien: https://www.linformaticien.com/magazine/infra/64028-vsora-me...
Solutions Numériques: https://www.solutions-numeriques.com/vsora-franchit-un-cap-a...
There’s also an industrial manufacturing partnership with GUC: https://www.design-reuse.com/news/202529700-vsora-and-guc-pa...
Strategically, having a European AI inference chip matters. The US has already threatened export limits to Europe, and China has shown similar behavior (e.g., Nexperia). Building local supply is important.
Calling this vaporware makes no sense: tape-out + published roadmap = real, not slides.
I agree that the comments here are surprisingly superficial in their complaints, but I guess it the typical bike-shedding, people don't have technical points to nitpick or the experience to judge the actual product, so from their US-based point of view, they find something else to hook on to, even when there are facts like concrete partnerships making it clear it isn't vaporware, they just have to say something.
I want to believe: let's see that software stack working effectively.
How do you tell the difference? Wait infinitely long and see if it appears?
If those things are true in ~6 months, then I'll join the crowd here who are overly pessimistic at this moment, but until then, as most of the time, I'll give them benefit of the doubt.
Where do you see the negativity?
I don't believe labeling healthy skepticism and criticism as negativity to farm artificial sympathy in retaliation, does any good to anyone.
Humans have pattern recognition capabilities for a reason, and if a company is triggering that in them, then it's best expressed why(probably because they saw this MO before and got burned) instead of just cheerleading the unknown for fake positivity.
First comment: "Looks expensive, I'm guessing"
Second comment: "Probably vaporware"
6th comment: "They haven't disclosed any release date, Lots of chip startups start as this kind of vaporware" (they did literally just enter fabrication it seems)
10th comment: "So far, they just talk about it."
Maybe it looks differently now, after 14 hours since the submission was made, but initially yesterday, most of the comments were unfounded (and poorly researched) criticism.
The bottleneck for inference right now isn't just raw FLOPS or even memory bandwidth—it's the compiler stack. The graveyard of AI hardware startups is filled with chips that beat NVIDIA on specs but couldn't run a standard PyTorch graph without segfaulting or requiring six months of manual kernel tuning.
Until I see a dev board and a working graph compiler that accepts ONNX out of the box, this is just a very expensive CGI render.
That seems like not much compared to the hundreds of billions of dollars US companies currently invest into their AI stack? OpenAI pays thousands of engineers and researchers full time.
The outcome is that most of custom chips end up not being sold on the open market; instead their manufacturers run them themselves and sell LLM-as-a-service. E.g. Cerebras, Samba Nova, and you could count Google's TPUs there too.
indeed no mention of PyTorch in their website...honestly it looks a bit scammy as well
Edit: It kind of looks like there's no silicon anywhere near production yet. Probably vaporware.
Also, the 3D graphic of their chip on a circuit board is missing some obvious support pieces, so it's clearly not from a CAD model.
Lots of chip startups start as this kind of vaporware, but very few of them obfuscate their chip timelines and anticipated release dates this much. 5 years is a bit long to tapeout, but not unreasonable.
Most start-ups innovate on the compute side, whereas the techno needed for state of the art communications is not common, and very low-level: plenty of analog concerns. The domain is dominated by NVidia and Broadcom today.
This is why digital start-ups tend to focus on inference. They innovate on the pure digital part, which is compute, and tend to use off-the-shelf IPs for communications, so not a differentiator and likely below the leaders.
But in most cases coupling a computation engine marketed for inference with state of the art communications would (in theory) open the way for training too. It's just that doing both together is a very high barrier. It's more practical to start with compute, and if successful there use this to improve the comms part in a second stage. All the more because everyone expects inference to be the biggest market too. So AI start-ups focus on inference first.
It doesn't have to compete on price 1:1. Ever since Trump took office, the Europeans woke up on their dependence on USA who they no longer regard as a reliable partner. This counts for defense industry, but also for critical infrastructure, including IT. The European alternatives are expected to cost something.
It sounds nice, but how much is it?
https://www.opensourceforu.com/2025/11/ainekko-turns-esperan...
From their web page Euclyd is a "many small cores" accelerator. Doing good compilation toolchains for these to get efficient results is a hard problem, see many comments on compilers for AI in this thread.
Vsora approach is much more macroscopic, and differentiated. By this I mean I don't know anything quite like it. No sea of small cores, but several more beefy units. They're programmable, but don't look like a CPU: the HW/SW interface is at a higher level. A very hand-wavy analogy with storage would be block devices vs object storage, maybe. I'm sure more details will surface when real HW arrive.
Hope they can figure out software, but what im seeing isn't super-promising
> To streamline development and shorten time-to-market, VSORA embraces industry standards: our toolchain is built on LLVM and supports common frameworks like ONNX and PyTorch, minimizing integration effort and customer cost.
Did they generate their website with their own chips or on Nvidia hardware?
