For those that use Blender, in their section about Blender:
> We hope that, in the future, there will be real options other than NVIDIA for GPU-based rendering, as it is an area where competition is nearly non-existent.
And Checking opendata.blender.org, a NVIDIA GeForce RTX 4080 Laptop GPU scores 5301.8, while Intel Arc Pro B70 is still at 3824.64.
So there is still a bit more to go before Intel GPUs perform close to NVIDIA's.
Also the first section I jumped to :) To Intel's credit, seems they're slowly improving, the section starts with:
> Over the last year or two, Intel has worked to deliver serious optimizations for and compatibility with Blender GPU rendering on its Arc GPUs. Although NVIDIA has long held an advantage in the application, our last time looking at Intel’s cards indicated ongoing improvements. This round of testing is no different. We found that the Arc Pro B70 provided more than twice the performance of the B50, also beating the R9700 by 9%.
Time to first token is a very important performance metric, as I figured out using a Mac Studio M3 Ultra (that is quite slow on this aspect).
But 32GB for a TDP of 230W is perhaps not super interesting. Especially because you probably want to have more than one card. It's a lot of heat. You could use the cards for heating up a building, but heatpumps exist.
A lot of the TDP is reserved for running the shader units at full-power. My RTX 3070 Ti only pulls ~110w of it's 320w running CUDA inference on Gemma 26b and E4B.
It's not that it's reserving power, but rather that you hit some bottleneck on a 3070 Ti before running into thermal limits-- it's likely limited by either tensor core saturation or RAM throughput. Running the workload with Nvidia's profiling tools should make the bottleneck obvious.
Generally the bottleneck is RAM throughput. Inference, in particular token generation, especially on a single user instance, is not all that computationally complex; you're doing some fairly simple calculations for each parameter, the time is dominated by just transferring each parameter from RAM to the cores. A 31B dense model like Gemma 4 has to transfer 31B parameters (at 16 bits per parameter for the full model, though on consumer hardware people generally run 4-8 bit quantizations) from RAM to the cores, that's a lot of memory transfer.
Prompt processing or parallel token generation can do a bit more work per memory transfer, as you can use the same weights for a few different calculations in parallel. But even still, memory bandwidth is a huge factor.
I was looking into this for LLMs but it's clearly a graphics-processing focused card. The memory bandwidth is too low for that much RAM to be useful in an LLM context. The 5090 I have has the same amount of RAM but far more bandwidth and that makes it much more useful.
Just ran llama-bench at home with the similar priced AMD AI PRO R9700 32G. The phoronix numbers look extremely low? Probably I misunderstand their test bench. Anyway, here are some numbers. Maybe someone with access to a B70 can post a comparison.
I would like one for the vram but I am sure they will be unobtainable after the initial stock sells out as I assume they were produced before the RAM prices went up.
What do you mean, are they still making GPUs? This is a discrete GPU that has just recently been released, and it's one of the most popular GPUs in its class at the moment, due to 32 GiB of RAM for under $1000, which makes it great for LLM inference.
Intel always had that habit of starting an internal conflict whenever whatever potential alternative revenue sources start to threaten their internal dependence on x86
They'll always have iGPUs so whether or not they stay in the dGPU market depends mostly on whether or not people buy them. So they might not, whole market seems to be moving to SoCs/APUs/whatever you want to call them.
Not only will they always have iGPUs, but also cannot give up on advancing their datacenter AI GPUs (the next being Jaguar Shores). They need both of those far more than consumer or prosumer dGPUs, but that means they are committed to Big GPU work and Small GPU work.
Since they will have both of those big and small "bookends" of GPU architectures, it is a question of whether they see benefits in maintaining an accessible foothold in the midmarket ecosystem. I could make an argument for both sides of that, but obviously the decision is not up to me.
Don't think that's true. The drivers are bad (not sure terrible is fair, they have improved a lot) esp for older directx etc games. But Vulkan support is pretty good and that's all you need for LLMs really.
The drivers often need per game optimisations these will be missing but I doubt Intel would nerf them, just rely on you not paying a lot for RAM the game won't use.
I actually meant it in a different way. I would get it for local AI stuff, but being able to game on it would be a huge plus, otherwise I would need two different machines.
It looks like, if one can afford it, the R9700 is worth the extra money.
I read that Intel is getting out of the dGPU space, but then again, their iGPUs are really getting good. I can't understand why they'd give up the space when the AI market is so insane.
Rumors of their exit from dGPU predate Battlemage. So I wouldn't put a ton of credence to them. But Intel's is quite talented at snatching defeat from the jaws of victory.
I hope not. They’ve been flip flopping too much and the market needs more dGPU competition.
The team working on drivers is doing a good job playing catch up and I hope intel will continue to invest in cards that focus on graphics workloads and not just on AI inference.
Why are they still using their old Xe2/Battlemage architecture rather than their new Xe3/Celestial? They already used it in their Panther Lake chipset.
> We hope that, in the future, there will be real options other than NVIDIA for GPU-based rendering, as it is an area where competition is nearly non-existent.
And Checking opendata.blender.org, a NVIDIA GeForce RTX 4080 Laptop GPU scores 5301.8, while Intel Arc Pro B70 is still at 3824.64.
So there is still a bit more to go before Intel GPUs perform close to NVIDIA's.
> Over the last year or two, Intel has worked to deliver serious optimizations for and compatibility with Blender GPU rendering on its Arc GPUs. Although NVIDIA has long held an advantage in the application, our last time looking at Intel’s cards indicated ongoing improvements. This round of testing is no different. We found that the Arc Pro B70 provided more than twice the performance of the B50, also beating the R9700 by 9%.
But 32GB for a TDP of 230W is perhaps not super interesting. Especially because you probably want to have more than one card. It's a lot of heat. You could use the cards for heating up a building, but heatpumps exist.
Prompt processing or parallel token generation can do a bit more work per memory transfer, as you can use the same weights for a few different calculations in parallel. But even still, memory bandwidth is a huge factor.
I have a pair of them with a 9480 and the only thing I have to do is keep the cache happy.
Tried to use the same model as the article:
llama-bench -m gpt-oss-20b-Q8_0.gguf -ngl 999 -p 2048 -n 128
AMD R9700 pp2048=3867 tg128=175
And a bigger model, because testing a tiny model with a 32GB card feels like a waste:
llama-bench -m Qwen3.6-27B-UD-Q6_K_XL.gguf -ngl 999 -p 2048 -n 128
AMD R9700 pp2048=917 tg128=22
Which might not sound like much, but 2months in llm time is a long time, especially regarding support for new hardware like the r9700.
Since they will have both of those big and small "bookends" of GPU architectures, it is a question of whether they see benefits in maintaining an accessible foothold in the midmarket ecosystem. I could make an argument for both sides of that, but obviously the decision is not up to me.
Intel looks like they'll leave the dedicated GPU space, so it's a bit doubtful if the drivers will ever catch up.
Or the makers intentionally nerf them, in order to better segment the markets/product lines?
I read that Intel is getting out of the dGPU space, but then again, their iGPUs are really getting good. I can't understand why they'd give up the space when the AI market is so insane.
The team working on drivers is doing a good job playing catch up and I hope intel will continue to invest in cards that focus on graphics workloads and not just on AI inference.