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Intel Arc Pro B70 Review
- 100ms 6mo agoThese seem amazing for hobbyist, but that TDP given the perf might be an issue deploying a lot of them
- zrm 6mo agoIts performance is pretty unbalanced. If you're using it for the couple of things that it's good at, the TDP is competitive.
- tempest_ 6mo agoI 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.
- XCSme 6mo agoCan you use those AI cards for gaming too? Or the makers intentionally nerf them, in order to better segment the markets/product lines?
- ZiiS 6mo agoThe 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.
- XCSme 6mo agoI 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.
- ZiiS 6mo agoMuch as I want diversity; a 3090 would be a billion times better for games and can probably hold its own for a broader AI workload. Anything other then running highly quantised models that don't fit in 24GB with realativly small contexts.
- XCSme 6mo agoA 3090 is what I have now. But I hope to somehow have 48Gb or 64GB VRAM in a GPU that's also gaming-ready. I was looking for maybe getting a mac studio for this reason, but I don't think a mac is really good for for gaming.
- MrDrMcCoy 6mo agoIt'll work just fine for gaming. It's what the B770 would have been if it had 32GB RAM and ever got released.
- wmf 6mo agoThey nerf gaming cards to make money on the pro cards. Since this is a pro card it's not nerfed.
- MostlyStable 6mo agoIs Intel still making GPUs? I have heard so many conflicting things about will they/won't they stay in the market.
- dismalaf 6mo agoThey'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.
- chao- 6mo agoNot 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.
- throwaway85825 6mo agoThey're working with nvidia to use their GPU tiles in mobile products.
- 2OEH8eoCRo0 6mo agoI don't know what to believe when it comes to Intel news because they have so many haters.
- numpad0 6mo agoIntel always had that habit of starting an internal conflict whenever whatever potential alternative revenue sources start to threaten their internal dependence on x86
- lambda 6mo ago
- speedgoose 6mo agoTime 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.
- bigyabai 6mo agoA 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.
- Scaevolus 6mo agoIt'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.
- lambda 6mo agoGenerally 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.
- deleted 6mo ago[deleted]
- SparkyMcUnicorn 6mo agoHere are some llama.cpp benchmarks for it: https://www.phoronix.com/review/intel-arc-pro-b70-linux/3 https://www.phoronix.com/review/intel-arc-pro-b70-linux/3
- zargon 6mo agoAlso from phoronix, a comparison with AMD R9700 and RTX 6000 Ada (because Nvidia has not sent them a blackwell card): https://www.phoronix.com/review/intel-arc-pro-b70/2 https://www.phoronix.com/review/intel-arc-pro-b70/2
- canpan 6mo agoJust 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. 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
- deleted 6mo ago[deleted]
- andy_xor_andrew 6mo agothe build they use is from February, over two months old: https://github.com/ggml-org/llama.cpp/releases/tag/b8121 https://github.com/ggml-org/llama.cpp/releases/tag/b8121 Which might not sound like much, but 2months in llm time is a long time, especially regarding support for new hardware like the r9700.
- Mindless2112 6mo agoAs of b8966, it is still not great. | model | size | params | backend | ngl | test | t/s | | --------------------- | --------: | ------: | ------- | --: | -----: | -------------: | | gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | SYCL | 999 | pp2048 | 851.81 ± 6.50 | | gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | SYCL | 999 | tg128 | 42.05 ± 1.99 | | gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | Vulkan | 999 | pp2048 | 2022.28 ± 4.82 | | gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | Vulkan | 999 | tg128 | 114.15 ± 0.23 | | qwen35 27B Q6_K | 23.87 GiB | 26.90 B | SYCL | 999 | pp2048 | 299.93 ± 0.40 | | qwen35 27B Q6_K | 23.87 GiB | 26.90 B | SYCL | 999 | tg128 | 14.58 ± 0.06 | | qwen35 27B Q6_K | 23.87 GiB | 26.90 B | Vulkan | 999 | pp2048 | 581.99 ± 0.86 | | qwen35 27B Q6_K | 23.87 GiB | 26.90 B | Vulkan | 999 | tg128 | 10.64 ± 0.12 | Edit: I've no idea why one would use gpt-oss-20b at Q8, but the result is basically the same: | model | size | params | backend | ngl | test | t/s | | --------------------- | --------: | ------: | ------- | --: | -----: | -------------: | | gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | SYCL | 999 | pp2048 | 854.16 ± 6.06 | | gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | SYCL | 999 | tg128 | 44.02 ± 0.05 | | gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | Vulkan | 999 | pp2048 | 2022.24 ± 6.97 | | gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | Vulkan | 999 | tg128 | 114.02 ± 0.13 | Hopefully, support for the B70 will continue to improve. In retrospect, I probably should have bought a R9700 instead...
- driverdan 6mo agoFrom what I've read the Intel drivers are terrible and holding back using them for LLMs.
- 999900000999 6mo agoEveryone has terrible drivers here aside from Nvidia. Intel looks like they'll leave the dedicated GPU space, so it's a bit doubtful if the drivers will ever catch up.
- reallytD91 6mo agoWhat makes you think Intel will leave the GPU space?
- 999900000999 6mo agohttps://www.tomshardware.com/pc-components/gpus/intel-has-reportedly-killed-discrete-gaming-gpus-for-the-upcoming-xe3p-arc-celestial-family-gaming-gpu-remains-uncertain-even-for-the-next-gen-xe4-druid-lineup-that-lands-in-2027 https://www.tomshardware.com/pc-components/gpus/intel-has-re... I've seen several stories like this. Which is a shame since Intel offers the best value GPUs on the market. I guess it's possible they'll still make workstation GPUs while skipping the consumer market.
- reallytD91 6mo agoI guess they need to up their marketing game, as a lot of people I know are still unaware of Intel GPUs. It's either Nvidia or AMD.
- martinald 6mo agoDon'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.
- marshray 6mo ago
- cubefox 6mo agoWhy are they still using their old Xe2/Battlemage architecture rather than their new Xe3/Celestial? They already used it in their Panther Lake chipset.
- phonon 6mo agoThat's coming out in https://www.phoronix.com/review/intel-crescent-island https://www.phoronix.com/review/intel-crescent-island by around the end of the year.
- fc417fc802 6mo agoAnother comment here claims Celestial is cancelled. Has Intel indicated their intentions for the consumer dGPU space?
- cubefox 6mo agoThere are only rumours apparently: https://www.club386.com/intel-arc-celestial-cancelled-leak/ https://www.club386.com/intel-arc-celestial-cancelled-leak/
- deleted 6mo ago[deleted]
- wmf 6mo agoIt looks like B70 was delayed 1-2 years for some reason.
- numpad0 6mo ago$950 for 23TF fp32? Have GPU performance grew in past 5-10 years at all?
- kinow 6mo agoFor 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.
- embedding-shape 6mo agoAlso 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%.
- kinow 6mo agoYeah, I checked an Intel GPU some years ago and I think it was scoring near 1000 or below in Blender's open data. Glad it's slowly improving, although I have to check if the price is also increasing or not, although I suspect it must still be cheaper than the other options.
- keyle 6mo agoThis is because Blender is in fact using CUDA?
- Joel_Mckay 6mo agoThe key feature on intel platforms is the hardware de-noise acceleration (NVIDIA OptiX also works well.) Note, AMD OpenCL works quite well for some renderings, but blender flamenco likes consistent cluster hardware. For 8k HDR10 media or 3+ screens the rtx 5090 32G model is going to be the minimum card people should buy. Just because you see 4 DP ports, doesn't mean the card can push bit-rates needed to fill an HDR10 display >60Hz. The Mac Studio Pro unified >512GB ram/vram is a better LLM lab solution (Apple recently NERF'd it to 256GB.) Who cares if a task completes a bit slower, it doesn't matter given the lower error rates... and not costing $14k like an rtx 6000. =3 Great tutorial on getting blender to behave on mid-grade PC and laptops etc. : https://www.youtube.com/watch?v=a0GW8Na5CIE https://www.youtube.com/watch?v=a0GW8Na5CIE
- unethical_ban 6mo agoIt 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.
- timschmidt 6mo agoRumors 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.
- yurishimo 6mo agoI 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.
- ycui7 6mo agoExiting dGPU for gaming, but staying in the LLM world.
- arjie 6mo agoI 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.
- Mindless2112 6mo agoCompared to a B70, a 5090 is 1x the memory with 3x the bandwidth at 4x the price. Yeah, the 5090 is better, but you're paying for it.
- arjie 6mo agoOn actual market it’s $1100 vs $3200 now, right? I actually got mine at $2200 at cost in the before days.
- Mindless2112 6mo agoCurrent lowest price for a new card on Newegg: $949.99 vs $3,699.99.
- arjie 6mo agoWow, 5090 prices have exploded. Thanks for looking. I should have known by hardware price intuition is broken.
- cmxch 6mo agoIt’s 32gb for people who can’t go for scalped 5090s but have a 3090 budget. I have a pair of them with a 9480 and the only thing I have to do is keep the cache happy.
- fluoridation 6mo agoEh. Trading CUDA for 8 more gigs seems like bad deal, unless you know absolutely for certain what you want to run will run on it.
- 2001zhaozhao 6mo agoThere's a tradeoff between dense models and MoEs on memory usage vs. compute for the same quality. 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.
- BoredPositron 6mo agoI am working mostly with image models so we do a lot of fun times and the card fits perfectly here. Performance isn't great but it can just tug along in the background.àp
- storus 6mo agoDGX Spark is at the compute level of 5070. Its main issue is low memory bandwidth, i.e. it has quite fast token prefill but awful token generation. Strix Halo is just slow on every metric and used to be a cheap way to get 128GB unified RAM (now its prices are comparable to DGX Spark).
- tehologist 6mo agoI have one, this isn't true. The wattage of a 5070 is about 300. The spark entire unit runs at 200 watts max. In reality it runs like a rtx 5060 with lots of vram. Very good for training, okay for inferencing if you are running batch jobs and don't mind waiting.
- jbellis 6mo agoHow should I update my simplistic understanding that decode is bw-bound with these results that show the B70 decoding faster than a 4090 (about 50% more bw)?
- rao-v 6mo agoI doubt you'd get the same sort of result on a modern-ish MOE or dense model via a more standard inference engine like llama.cpp or VLLM. I don't think MLPerf is a reasonable benchmark at this point. 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_rtx_3090_vs_intel_arc_pro_b70_llamacpp/ https://www.reddit.com/r/LocalLLaMA/comments/1st6lp6/nvidia_...
- luckydata 6mo agothis review was essentially pointless, they reviewed the card for a ton of workloads nobody in their right mind would pick it for, and left out the only use case where it makes sense. great job?
- CoastalCoder 6mo agoYou may have a valid technical point. If you find a friendlier way to phrase it, you may find more people willing to discuss it.
- luckydata 5mo agono discussion was needed, it was a statement. the tone is dismissive because the video deserved it.
- mrweasel 6mo agoHow you would you know not to pick this card for these types of workloads, without benchmarking it?
- luckydata 5mo agolet's benchmark a truck for doing track days. pointless.
- userbinator 6mo agoIt should be possible to use the VRAM as extra swap space, when you're not using it for AI or gaming or anything else. 32GB is already more than a lot of computers have as just regular RAM, even sufficient to hold an OS installation: https://www.tomshardware.com/news/lightweight-windows-11-runs-entirely-in-gpus-vram https://www.tomshardware.com/news/lightweight-windows-11-run...
- dwoldrich 6mo agoHi Intel, I'm itching to buy an Xe3P! Or, Nova Lake? Crescent Island? Celestial? Jaguar Shores? Whatever the hell you name it doesn't matter to me, I just want a workstation with one of them bad boys attached to 160GB of RAM for legit inference power! I've been saving my money not paying for Claude Code so I can run my own agentic coding setup at home on yours. Please don't charge too much for the workstation class card if you can at all manage it. Maybe give us a discount to preorder? Please don't price a regular consumer like me out of the market! Also, I am speculating integer based models will become hot due to lower memory and power requirements. Will the Xe3P be able to do integer-based math inference to use all that RAM to even greater effect?
- deleted 6mo ago[deleted]
- als0 6mo ago> Please don't charge too much for it Intel wouldn’t decide to do this even to save their own life
- dwoldrich 6mo agoThe whole rest of the industry seems blind to the possibility of excellent personal/private agentic coding. There is a chance Intel could capitalize on that and steal a ton of mindshare in a flash. Maybe a slim chance based on past performance, but it's there.
- als0 6mo agoIntel's strategy has consistently been that they do not consider doing any kind of business unless they earn at least 25% margin on each sale.
- ycui7 6mo agoIt is weird that the reviewer does not mention RTX PRO 6000 96GB, but mentioned RTX PRO 5000 72GB. 72GB RTX PRO 5000 is a special order, and much less people are aware of it. RTX PRO 6000 is known by mostly everyone in the LLM world. I cannot understand why would a tech reviewer do that.
- ycui7 6mo agoIntel Arc B70 when released, can only produce 1/3 of the token of RTX PRO 4500. Well, it also cost 1/3 of RTX PRO 4500. It lacked software support the for the primary target application, running LLM. The officially supported vllm fork is 6 version behind mainline. It did not run the latest hot new open models on huggingface. Parallel two of B70 reduce token rate, not improve it. So, the software behind B70 is basically so far behind.
- muyuu 6mo agoThere are nonlinearities to exploit in that calculus. Given enough VRAM to host a larger model that you're targeting, just the size can push you past the usability threshold at a much better price.
- ycui7 6mo agoProblem is the more B70 you have, the slower the inference it gets(due to terrible software atm). A single B70 is almost barely faster than CPU inference. If you have 4 B70, you might as well run interference on CPU and be faster with cheaper DDR5 instead of GDDR6.
- adrian_b 6mo agoFor what you say to be useful, please specify what sowftware you have used with B70, including its version. Hardware-wise a B70 should be significantly faster than any of the available CPUs at ML inference. If it was not so in your tests, that must really be a software problem, so you must identify the software, for others to know what does not work.
- ycui7 6mo agoWhen you get 4 of these, the idle power alone is 120W. That is a lot of electricity if left on 24/7. At that power consumption, you also end up being more expensive than API calls and many times slower. It starts to feel very stupid to run local interference. If the client is very keen on privacy, then they can pay for the NVIDIA. I end up returning my B70s, and bought RTX PRO 6000.
- greybcg 6mo agoSomething that is also cool with these cards is proper SR-IOV without hassle. Arc pro cards make for nice graphical acceleration devices for vms. I know ai gets all the hype but I also appreciate being able to accelerate multiple workstations with a single gpu and still get decent frametimes.
- rekoil 6mo agoI bought an Arc A770 expecting that this feature would materialise based on it being available on other contemporary Intel GPUs, but it never did. Does the B70 definitely support SR-IOV from day one?
- greybcg 6mo agoI have unfortunately only confirmed 24 VFs on an Asrock Creator B60 Pro, later nerfed to 8 VFs after an intel update. I think someone tested the B70 Pro on the Level1Tech forums. I might consider buying a B70 myself just to confirm it.
- nirav72 6mo agoThe B50 certainly does. So Likely same for B70. https://youtu.be/xii8bqmE6jk?si=EqpzZYA-z-46F697 https://youtu.be/xii8bqmE6jk?si=EqpzZYA-z-46F697
- wg0 6mo agoCan we not have a PCIe card that's ASIC (and isn't GPU) with even DDR 4 or DDR 5 memory (Let's say 128 GB) onboard and being able to shove four of them on a consumer grade motherboard and then being utilized in parallel? Noob question.
- alex43578 6mo agoDDR4 or 5 would really run into bandwidth and value issues. Bandwidth on that memory interface and setup for dual channel would be significantly worse than Strix Halo, which already exists and could be an entire compute setup with no need for an ASIC.
- zokier 6mo agoConsumer grade motherboards generally have 20ish pcie lanes, which more or less boils down to 16x gpu and 4x nvme + something for random peripherals. You'd need something like threadripper to do better.