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Vsora Jotunn-8 5nm European inference chip

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Re: Vsora Jotunn-8 5nm European inference chip

#21

Post by NaomiLehman »

mg wrote: post #15
leo_e wrote: post #14

Impressive numbers on paper, but looking at their site, this feels dangerously close to vaporware. 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 d…

Six months of one developer tuning the kernel? 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.

more like 100 developers for 2 years

Re: Vsora Jotunn-8 5nm European inference chip

#24

Post by unwind »

bangaladore wrote: post #12

I love that the JS loads so slow on first load that it just says "The magic number: 0 /tflops"

It loaded fine for me, but that slash before the unit was a bit smelly. :| Just a tiny edit, but it's a rather core part of their message so they should probably notice and format it correctly before publishing.

Re: Vsora Jotunn-8 5nm European inference chip

#25

Post by ndom91 »

I'll believe it when I see it wishing them the best!

> 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.

Re: Vsora Jotunn-8 5nm European inference chip

#26

Post by ddalex »

rq1 wrote: post #6
all2 wrote: post #2

288GB RAM on board, and RISC V processors to enable the option for offloading inference from the host machine entirely. It sounds nice, but how much is it?

The next generation will include another processor to offload the inference from the RISC V processors used to offload inference from the host machine.

The next next generation will include memory to offload memory from the on chip memory to the memory on memory (also known as SRAM cache)

Re: Vsora Jotunn-8 5nm European inference chip

#28

Post by vlorr »

ano-ther wrote: post #17

I don’t get the negativity. 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 c…

The negativity doesn’t make much sense. The specs are strong, and the chip already taped out in October that’s a concrete milestone, not vaporware. Hardware of this class always takes months between tape-out and dev boards. Official announcement: https://vsora.com/vsora-announces-tape-out-of-game-changing-...

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.

Re: Vsora Jotunn-8 5nm European inference chip

#30

Post by vlovich123 »

leo_e wrote: post #14

Impressive numbers on paper, but looking at their site, this feels dangerously close to vaporware. 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 d…

Inference accelerators are not where Nvidia is maintaining their dominance afaik.
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