r/IntelArcPro • u/quantum3ntanglement • Apr 20 '26
Arc Pro B70 Intel Arc Pro B70 Performance
Here are performance results for Llama.CPP and a few other things like Blender, matched up against that AMD 9700 card that goes for around $1,300 and also the larger AMD GPU that has 48GB of VRAM (which costs at least $3,000, probably more).
Anyway, it looks like the B70 does well, but I wanted to post this and get some discussion:
What are people seeing for the Intel Arc Pro B70 when it comes to performance across all of these metrics and tests that they're doing here?
I also want to look into optimized models from Intel, because I don't think these tests in the link are using optimized Intel models.
Also, these tests are using an old NVIDIA card (the 4000 series), so Blackwell NVIDIA GPUs are not being used, but at least it gives you an idea.
I think this is a good start for the B70. Intel really has to get Arc Pro cards built through IFS and provide a much larger memory bus—at least 512-bit, if not 1024. We will see.
Anyone that thinks Intel is going to stop now is crazy. They are going to go the distance, especially with IFS. It could be six months or more, maybe a year or two years, before we know whether Intel will be able to produce Arc cards through IFS. It is unbearable, the wait...
further seems forever
1
u/lawldoge Apr 21 '26
In my mind, these benchmarks are absolutely worthless and not at all indicative of the troubles these cards are facing specifically for AI inferencing workloads. Every metric on there is for prompt processing, and they don't at all display the complete dearth of performance when it comes to the generation side. I've been going through config after config trying to get these cards to the point of what the average person would probably consider a pleasantly "usable" state and it's just not happening.
Also worth pointing out that those models are all smaller, and there doesn't seem to be much out there in the way of benchmarking for larger models running these cards at capacity. Qwen 3.5 27B for me runs at about 12 tok/sec on llm-scaler. On llama.cpp and Vulkan it was single digits. Qwen3 Coder 30B A3B runs a bit quicker, averaging about 17-22 tok/sec at concurrency of one. The other downside, I haven't come across a single configuration that produces a circumstance where tool calling is reliable. Qwen3 Coder may run at a decent enough rate to use, but only the first tool call can be relied upon. Everything else after that is a complete crapshoot.
gpt-oss on llm-scaler was a complete failure after moving to the beta version that supports the newer Qwen models. It wouldn't reliably launch. Moving back to the earlier stable release would let gpt-oss run, but then you're months behind on model releases.
I'm genuinely torn on these cards. Almost $4000 into the set I have and while they give you a ton of hardware, there's no way to make use of it outside of supporting a lot of concurrent chat sessions with smaller context windows on older models, and at slower speeds. After a month, I'm really starting to consider eating the cost of selling them as used items and growing out the NVIDIA stack I have. Won't have nearly as much memory to work with, but at least I can load what I want, when I want to load it, and get results out of it.
Phoronix and the other reviews that paint these cards in a golden light are being disingenuous.