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Discussion (56 Comments)Read Original on HackerNews
For example, Qwen3.5 27B and Qwen3.5 122B A10B have similar average performance across benchmarks. The 122B is much faster to run than the 27B (generates more tokens at the same compute). The 27B, on the other hand, uses ~4x less VRAM at low context lengths (less difference at high context lengths).
Right now, different hardware seems to be suited to different points in the dense vs. MoE balance. On one extreme is hardware like the DGX Spark and Strix Halo which have a lot of memory compared to compute performance and memory bandwidth, and are best-suited for MoE workflows. On the other extreme you have cards like RTX 5090 which have very high performance for the price but rather little memory, and is best suited for dense models.
The Arc Pro B70 seems to be the awkward middle. With 1-2 of these, you can run a ~30B dense model slowly, probably not fast enough to be useful interactively (you'd probably need a 5090 or 2x 3090 for that). Or, you can run a MoE model at high throughput, but probably not enough quality to support agentic workflows that actually use your throughput.
Why can't Intel look beyond this nonsense state of affair and build something with 1TB of RAM or more?
What I am trying to say, I am yet to see anything competitive in the market. Cards very much stalled in sub 100GB region and best corporations can do is throw something to run toy models and forget about it after a week.
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.
> 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%.
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.
Edit: Here is a simple llama.cpp compare where the token gen results match the rule of thumb.
https://www.reddit.com/r/LocalLLaMA/comments/1st6lp6/nvidia_...
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.
Or the makers intentionally nerf them, in order to better segment the markets/product lines?
Intel looks like they'll leave the dedicated GPU space, so it's a bit doubtful if the drivers will ever catch up.
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.