Intel "Crescent Island" GPU Die Shows Up: 32 Xe3P Cores and 256 XMX Engines

“Additional details include support for the FP4 data format alongside FP8, as well as large register files of 1 MB per core and an L1 cache of 512 KB per core. In total, there are 256 Vector engines and 256 of the new XMX engines, each divided into groups of eight per Xe3P core. Furthermore, the Xe3P lacks graphics processing elements for outputting imagery, as the silicon is designed purely for AI inference workloads that require long context and agentic tasks. A media engine with four encoders and four decoders is also included, which could assist with multimodal workloads where video generation and understanding are essential.


The accelerator is designed as a 350 W, air-cooled PCIe 5.0 x16 card, allowing it to fit into conventional server infrastructure without the need for dedicated liquid cooling. Intel designed this silicon with agents performing thousands of steps, handling a 100K context, maintaining that context, and reasoning, all while serving tokens at high speeds. We have yet to see what real-world performance looks like and what Xe3P graphics IP can do for AI processing.“

Source: https://www.techpowerup.com/354171/...ie-shows-up-32-xe3p-cores-and-256-xmx-engines

erek said:

“Additional details include support for the FP4 data format alongside FP8, as well as large register files of 1 MB per core and an L1 cache of 512 KB per core. In total, there are 256 Vector engines and 256 of the new XMX engines, each divided into groups of eight per Xe3P core. Furthermore, the Xe3P lacks graphics processing elements for outputting imagery, as the silicon is designed purely for AI inference workloads that require long context and agentic tasks. A media engine with four encoders and four decoders is also included, which could assist with multimodal workloads where video generation and understanding are essential.
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The accelerator is designed as a 350 W, air-cooled PCIe 5.0 x16 card, allowing it to fit into conventional server infrastructure without the need for dedicated liquid cooling. Intel designed this silicon with agents performing thousands of steps, handling a 100K context, maintaining that context, and reasoning, all while serving tokens at high speeds. We have yet to see what real-world performance looks like and what Xe3P graphics IP can do for AI processing.“

Source: https://www.techpowerup.com/354171/...ie-shows-up-32-xe3p-cores-and-256-xmx-engines

Click to expand...

Intel's biggest problem (like with AMD) is the CUDA moat.
It is not just about running code, you also need vendor support for said GPU and the application(s) it runs.

Most vendors I have dealt with have their appcliation tested and verified on CUDA/NVIDIA , going "off the path" is a very risky business move.

You do not want to tell you customer "Yeah, I tried to save costs on the hardware and used notn-certified/non-compliant hardware, so the service you are paying for is not working at the moment and the vendor offers zero support for me. I hope it wasn't business critical for you?"

littlegreenmenfromhome said:

Intel's biggest problem (like with AMD) is the CUDA moat.

Click to expand...

Only in a sense for the giant customer.

An openAI and others make their own language-compilers, generate .ptx themselve and let nvidia cuda-stack/compiler optmise from there, for them using intel-amd the second they would be cheaper-better would not be a big deal, there is so much money spent in inference that it would be worth the limited amount of trouble, it is different than for smaller customer.

Intel-AMD biggest problem is that the even if you do not rely on CUDA at all (i.e. made the AMD version well and it work well), it is often cheaper to buy Nvidia hardware and use it than get AMD hardware for free and use it and that despite Nvidia selling it to you with a 80%+ gross margin (which hide their level of giant dominance a lot), if AMD get twice as good and still give it for free, Nvidia can become back a 40% margin company and still win...

Even if you give a proven working solution, the Nvidia-CUDA one is still so much better.

The giant customer like amazon-openai-anthropic-google-meta already have their own chips/full ecosystem or working on them, they are not necessarily affraid-have issue to go without CUDA, they have the money-know how (already did it), that why all of them do use AMD hardware in some capacity, it is just vastly inferior at the moment.

And now, you have the cuda side of things, but also the supply chain-time to market in physical time advantage, Nvidia ability to reserve and use the HBM, copper, fiber and so on over the rest is getting bigger and bigger, as design get more complex-costly having bigger sales mean you are the only one able to do yearly revision and launch (like apple advantage vs AMD-Intel in the cpu space) and has you add dimensions factor, like how important networking-storage system-custom asic accelerator (upcoming sram qroq), there is 7 massive dimensions (each with a custom Nvidia chips) that goes into a full Rubin solution, Nvidia is ahead on all of them and it compound.

The lock-in software side aspect is more for the small-mid enterprise or at least not really what going on for the hyperscalers-frontier lab with the money and experience to make their affair run on everything, it is running better on Nvidia than forced to run on it, they all run fine on AMD (at least inference wise).