[tester756]

https://www.phoronix.com/review/intel-arc-pro-b50-linux

>Overall the Intel Arc Pro B50 was at 1.47x the performance of the NVIDIA RTX A1000 with that mix of OpenGL, Vulkan, and OpenCL/Vulkan compute workloads both synthetic and real-world tests. That is just under Intel's own reported Windows figures of the Arc Pro B50 delivering 1.6x the performance of the RTX A1000 for graphics and 1.7x the performance of the A1000 for AI inference. This is all the more impressive when considering the Arc Pro B50 price of $349+ compared to the NVIDIA RTX A1000 at $420+.

[swiftcoder]

IIRC, the RTX A1000 is an RTX 3050 8GB with ~10% of the shader cores disabled, retailing for double the price of a 3050?

I guess it's a boon for Intel that NVidia repeatedly shoots their own workstation GPUs in the foot...

[eYrKEC2]

They may not be disabling them maliciously -- they may be "binning" them -- running tests on the parts and then fusing off/disabling broken pieces of the silicon in order to avoid throwing away a chip that mostly works.

[swiftcoder]

Yep, that's likely the case - but they still charge double for the reduced-performance binned chip, just because it's a "professional" GPU (which, last I heard, really just means it can use the pro variant of the GPU drivers)

[derefr]

Funny enough, maybe the fusing itself (if they go a bit above-and-beyond on it) is exactly why it is a pro model.

I.e. maybe Nvidia say "if we're going to fuse some random number of cores such that this is no longer a 3050, then let's not only fuse the damaged cores, but also do a long burn-in pass to observe TDP, and then fuse the top 10% of cores by measured TDP."

If they did that, it would mean that the resulting processor would be much more stable under a high duty cycle load, and so likely to last much longer in an inference-cluster deploy environment.

And the extra effort (= bottlenecking their supply of this model at the QC step) would at least partially justify the added cost. Since there'd really be no other way to produce a card with as many FLOPS/watt-dollar, without doing this expensive "make the chip so tiny it's beyond the state-of-the-art to make it stably, then analyze it long enough to precision-disable everything required to fully stabilize it for long-term operation" approach.

[zamadatix]

Comparing price to performance in this space might not make much sense as it would seem. One of the (very few) interesting qualities in the A1000 is that it's single slot, low profile, workstation GPU. Intel kept the "powered by the PCIe slot" aspect, but made it dual slot and full height. Needing a "workstation" GPU in a tiny form factor (i.e. not meant to slot and power full sized GPUs) was something one could squeeze on price for, but the only selling point of this is the price.

[tizio13]

I think you might be mistaken on the height of the card, if you look at the ports they are mini-DP on a low profile bracket. The picture also states that it includes both types of brackets.

[zamadatix]

Great catch, Serve The Home has a stacked picture of the two cards and they are indeed both low profile https://www.servethehome.com/wp-content/uploads/2025/09/NVID.... If your SFF box/1u server has a 2-thick NIC slot it may well be great for that use case then!

[derefr]

I'm still waiting for one of Nvidia/AMD/Intel to realize that if they make an inference-focused Thunderbolt eGPU "appliance" (not just a PCIe card in an eGPU chassis, but a sealed, vertically-integrated board-in-box design), then that would completely free them from design constraints around size/shape/airflow in an ATX chassis.

Such an appliance could plug into literally any modern computer — even a laptop or NUC. (And for inference, "running on an eGPU connected via Thunderbolt to a laptop" would actually work quite well; inference doesn't require much CPU, nor have tight latency constraints on the CPU<->GPU path; you mostly just need enough arbitrary-latency RAM<->VRAM DMA bandwidth to stream the model weights.)

(And yeah, maybe your workstation doesn't have Thunderbolt, because motherboard vendors are lame — but then you just need a Thunderbolt PCIe card, which is guaranteed to fit more easily into your workstation chassis than a GPU would!)

[robotnikman]

GIGABYTE recently did this with their new 'AI' box

https://www.gigabyte.com/Graphics-Card/GV-N5090IXEB-32GD

[derefr]

That misses the "vertically integrated" part. (As does everything else right now, which was my point.)

The thing you linked is just a regular Gigabyte-branded 5090 PCIe GPU card (that they produced first, for other purposes; and which does fit into a regular x16 PCIe slot in a standard ATX chassis), put into a (later-designed) custom eGPU enclosure. The eGPU box has some custom cooling [that replaces the card's usual cooling] and a nice little PSU — but this is not any more "designing the card around the idea it'll be used in an enclosure" than what you'd see if an aftermarket eGPU integrator built the same thing.

My point was rather that, if an OEM [that produces GPU cards] were to design one of their GPU cards specifically and only to be shipped inside an eGPU enclosure that was designed together with it — then you would probably get higher perf, with better thermals, at a better price(!), than you can get today from just buying standalone peripheral-card GPU (even with the cost of the eGPU enclosure and the rest of its components taken into account!)

Where by "designing the card and the enclosure together", that would look like:

- the card being this weird nonstandard-form-factor non-card-edged thing that won't fit into an ATX chassis or plug into a PCIe slot — its only means of computer connection would be via its Thunderbolt controller

- the eGPU chassis the card ships in, being the only chassis it'll comfortably live in

- the card being shaped less like a peripheral card and more like a motherboard, like the ones you see in embedded industrial GPU-SoC [e.g. automotive LiDAR] use-cases — spreading out the hottest components to ensure nothing blocks anything else in the airflow path

- the card/board being designed to expose additional water-cooling zones — where these zones would be pointless to expose on a peripheral card, as they'd be e.g. on the back of the card, where the required cooling block would jam up against the next card in the slot-array

...and so on.

It's the same logic that explains why those factory-sealed Samsung T-series external NVMe pucks can cost less than the equivalent amount of internal m.2 NVMe. With m.2 NVMe, you're not just forced into a specific form-factor (which may not be electrically or thermally optimal), but you're also constrained to a lowest-common-denominator assumption of deployment environment in terms of cooling — and yet you have to ensure that your chips stay stable in that environment over the long term. Which may require more-expensive chips, longer QC burn-in periods, etc.

But when you're shipping an appliance, the engineering tolerances are the tolerances of the board-and-chassis together. If the chassis of your little puck guarantees some level of cooling/heat-sinking, then you can cheap out on chips without increasing the RMA rate. And so on. This can (and often does) result in an overall-cheaper product, despite that product being an entire appliance vs. a bare component!

[wrs]

Random observation: This is very similar to the rationale for the “trash can” Mac Pro.

[justsomehnguy]

> Gigabyte-branded 5090 PCIe GPU

The hottest one on the consumer market

> The eGPU box has some custom cooling

Custom liquid cooling to tame the enormous TDP

> and a nice little PSU

Yeah, an 850W one.

>were to design one of their GPU cards specifically and only to be shipped inside an eGPU enclosure that was designed together with it

And why they would do so?

Do you understand what it would drive the price a lot?

> at a better price(!)

With less production/sales numbers than a regular 5090 GPU? No way. Economics 101.

> the card being this weird nonstandard-form-factor non-card-edged thing

Even if we skip the small series nuances (which makes this a non-starter by the price alone), there is a little what some other 'nonstandard-form-factor' can do for the cooling - you still need the RAM near the chip... and that's all. You just designed the same PCIe card for the sake of it being incompatible..

> won't ... plug into a PCIe slot

Again - why? What this would provide what the current PCIe GPU lacks? BTW you still need the 16 lines of PCIe and you know which connector provides the most useful and cost effective way to do so? A regular 16x PCIe connector. That one you ditched.

> the card being shaped less like a peripheral card and more like a motherboard

You don't need to 're-design it from scratch', it's enough not to be constrained with a 25cm limit to have a proper air-flow along a properly oriented radiator.

> why those factory-sealed Samsung T-series external NVMe pucks

Lol: https://www.zdnet.com/article/why-am-i-taking-this-samsung-t...

[zamadatix]

The strength is the weakness here - if the appliance gets so little from plugging directly into the host system then requiring the appliance to plug in to the host system to work at all becomes more of a burden than a value.

[onlyrealcuzzo]

Theoretically, they should be able to launch something more competitive in that form-factor next year, though, right?

[zamadatix]

With this wattage I'm not sure why they went double slot in this generation. Maybe they thought having a few dB more silence was a more unique placement for the card or something. The thickness of a GPU largely comes from the cooler, everything else typically fits under the height of the display connectors, and this GPU could certainly work with a single slot cooler.

[bsder]

Put 32GB on that card and everybody would ignore performance issues.

With 16GB everybody will just call it another in the long list of Intel failures.

[Moto7451]

This is not that sort of workstation.

My first software job was at a place doing municipal architecture. The modelers had and needed high end GPUs in addition to the render farm, but plenty of roles at the company simply needed anything with better than what the Intel integrated graphics of the time could produce in order to open the large detailed models.

In these roles the types of work would include things like seeing where every pipe, wire, and plenum for a specific utility or service was in order to plan work between a central plant and a specific room. Stuff like that doesn’t need high amounts of VRAM since streaming textures in worked fine. A little lag never hurt anyone here as the software would simply drop detail until it caught up. Everything was pre-rendered so it didn’t need large amounts of power to display things. What did matter was having the grunt to handle a lot of content and do it across three to six displays.

Today I’m guessing the integrated chips could handle it fine but even my 13900K’s GPU only does DisplayPort 1.4 and up to only three displays on my motherboard. It should do four but it’s up to the ODMs at that point.

For a while Matrox owned a great big slice of this space but eventually everyone fell to the wayside except NVidia and AMD.

[tracker1]

It's already got 2x the ram and roughly 1.5x the performance of the more expensive NVidia competitor... I'm not sure where you are getting your expectations from.

[wqaatwt]

But that Nvidia competitor seems like a pointless and overpriced card compared to Nvidia’s consumer GPUs.

[tracker1]

Sure.. but even going consumer, for 16gb+, you can get an ARC A770 for $80 less, or an RX 9060 XT for a few dollars more... Will it perform better, I don't know. RTX 5060 Ti 16gb is about $70 more.

Prices from NewEgg on 16gb+ consumer cards, sold by NewEgg and in stock.

[snowram]

I wonder why everyone keep saying "just put more VRAM" yet no cards seem to do that. If it is that easy to compete with Nvidia, why don't we already have those cards?

[fwipsy]

Maybe because only AI enthusiasts want that much VRAM, and most of them will pony up for a higher-end GPU anyways? Everyone is suggesting it here because that's what they want, but I don't know if this crowd is really representative of broader market sentiment.

[vid]

There are a lot of local AI hobbyists, just visit /r/LocalLLama to see how many are using 8GB cards, or all the people asking for higher RAM version of cards.

This makes it mysterious since clearly CUDA is an advantage, but higher VRAM lower cost cards with decent open library support would be compelling.

[rdos]

There is no point in using a low-bandwidth card like the B50 for AI. Attempting to use 2x or 4x cards to load a real model will result in poor performance and low generation speed. If you don’t need a larger model, use a 3060 or 2x 3060, and you’ll get significantly better performance than the B50—so much better that the higher power consumption won’t matter (70W vs. 170W for a single card). Higher VRAM wont make the card 'better for AI'.

[bsder]

> There is no point in using a low-bandwidth card like the B50 for AI.

People actually use loaded out M-series macs for some forms of AI training. So, total memory does seem to matter in certain cases.

[robotnikman]

>2x 3060

Are there any performance bottlenecks with using 2 cards instead of a single card? I don't think any one the consumer Nvidia cards use NVlink anymore, or at least they haven't for a while now.

[vid]

Who said anything about the B50?

Plenty of people use eg 2, 4 or 6 3090s to run large models at acceptable speeds.

Higher VRAM at decent (much faster than DDR5) speeds will make cards better for AI.

[fwipsy]

r/LocalLLaMA has 90,000 subscribers. r/PCMasterRace has 9,000,000. I'll bet there are a lot more casual PC gamers who don't talk about it online than there are casual local AI users, too.

[fwipsy]

Sorry. r/LocalLLaMA has 533k; r/PCMasterRce has 16m.

[blkhawk]

because the cards already sell at very very good prices with 16GB and optimizations in generative AI is bringing down memory requirements. Optimizing profits means yyou sell with the least amount of VRAM possible not only to save the direct cost of the RAM but also to guard future profit and your other market segments. the cost of the ram itself is almost nothing compared to that. any intel competitor can more easily release products with more than 16GB and smoke them. Intel tries for a market segment that was only served by gaming cards twice as expensive up until now. this frees those up to be finally sold at MSRP.

[betimsl]

Right, but Intel is in no position to do that. So, they could play and put 32GB VRAM and also, why not produce one with 64GB just for kicks?

[Workaccount2]

If intel was serious about staging a comeback, they would release a 64GB card.

But intel is still lost in it's hubris, and still thinks it's a serious player and "one of the boys", so it doesn't seem like they want to break the line.

[bsder]

> If it is that easy to compete with Nvidia, why don't we already have those cards?

Businesswise? Because Intel management are morons. And because AMD, like Nvidia, don't want to cannibalize their high end.

Technically? "Double the RAM" is the most straightforward (that doesn't make it easy, necessarily ...) way to differentiate as it means that training sets you couldn't run yesterday because it wouldn't fit on the card can now be run today. It also takes a direct shot at how Nvidia is doing market segmentation with RAM sizes.

Note that "double the RAM" is necessary but not sufficient.

You need to get people to port all the software to your cards to make them useful. To do that, you need to have something compelling about the card. These Intel cards have nothing compelling about them.

Intel could also make these cards compelling by cutting the price in half or dropping two dozen of these cards on every single AI department in the US for free. Suddenly, every single grad student in AI will know everything about your cards.

The problem is that Intel institutionally sees zero value in software and is incapable of making the moves they need to compete in this market. Since software isn't worth anything to Intel, there is no way to justify any business action isn't just "sell (kinda shitty) chip".

[rocqua]

I believe that VRAM has massively shot up in price, so this is where a large part of the costs are. Besides I wouldn't be very surprised if Nvidia has such strong market share they can effectively tell suppliers to not let others sell high capacity cards. Especially because VRAM suppliers might worry about ramping up production too much and then being left with an oversupply situation.

[kokada]

This could well be the reason why the rumored RDNA5 will use LPDDR5X/LPDDR5X instead of GDDR7 memory, at least for the low/mid range configurations (the top-spec and enthusiast configurations AT0 and AT2 configurations will still use GDDR7 it seems).

[FuriouslyAdrift]

AFAIK, RDNA5 has been cancelled as AMD is moving back to a unified architecture with their Instinct and Radeon lines.

[kokada]

It is not really clear if it will be called as UDNA or RDNA5, I was just referring to the next-gen graphics architecture from AMD and referring as RDNA5 is just clearer that this is the next-gen architecture.

[GTP]

> Especially because VRAM suppliers might worry about ramping up production too much and then being left with an oversupply situation.

Given the high demand of graphic cards, is this a plausible scenario?

[williamdclt]

I don't really know what I'm talking about (whether about graphic cards or in AI inference), but if someone figures out how to cut the compute needed for AI inference significantly then I'd guess the demand for graphic cards would suddenly drop?

Given how young and volatile this domain still is, it doesn't seem unreasonable to be wary of it. Big players (google, openai and the likes) are probably pouring tons of money into trying to do exactly that

[rtrgrd]

I would suspect that for self hosted LLMs, quality >>> performance, so the newer releases will always expand to fill capacity of available hardware even when efficiency is improved.

[robotnikman]

There does seem to be a grey market for it in China. You can buy cards where they swap the memory modules with higher capacity ones on Aliexpress and ebay.

[danielEM]

Ryzen AI max+ 395 128GB can do 256GBps so lets put all these "ifs" to bed once for all. That is absolutely no brainer to drop more RAM as long as there is enough bits in address space of physical hardware. And there usually is, as same silicons are branded and packaged differently for commercial market and for consumer market. Check up how chinese are doubling 4090s RAM from 24 to 48GB.

[PunchyHamster]

Because if they do their higher models will sell worse.

[qudat]

I'm willing to bet there are technical limitations to just "adding more VRAM" to all these boards.

[agilob]

AMD is growing up into doing that, they have a few decent cards with 20Gb and 24Gb now

[izacus]

Because:

- less people care about VRAM than HN commenters give impression of

- VRAM is expensive and wouldn't make such cards profitable at the HN desired price points

[colechristensen]

#3 player just released something that compares well with price/performace ratio compared to #1 player's release from a year and a half ago... yep

[tinco]

No. The A1000 was well over $500 last year. This is the #3 player coming out with a card that's a better deal than what the #1 player currently has to offer.

I don't get why there's people trying to twist this story or come up with strawmen like the A2000 or even the RTX5000 series. Intel's coming into this market competitively, which as far as I know is a first, and it's also impressive.

Coming into the gaming GPU market had always been too ambitious a goal for Intel, they should have started with competing in the professional GPU market. It's well known that Nvidia and AMD have always been price gouging this market so it's fairly easy to enter it competitively.

If they can enter this market successfully and then work their way up on the food chain then that seems like good way to recover from their initial fiasco.

[colechristensen]

NVIDIA is looking for profit, Intel is looking for market share, the pricing reflects this. Of course your product looks favorable to something released April 2024 when you're cutting pricing to get more attention.

[apwell23]

helps to be funded by govt that doesn't care about profits

[blagie]

Well, no. It doesn't. The comparison is to the A1000.

Toss in a 5060 Ti into the compare table, and we're in an entirely different playing field.

There are reasons to buy the workstation NVidia cards over the consumer ones, but those mostly go away when looking at something like the new Intel. Unless one is in an exceptionally power-constrained environment, yet has room for a full-sized card (not SFF or laptop), I can't see a time the B50 would even be in the running against a 5060 Ti, 4060 Ti, or even 3060 Ti.

[magicalhippo]

> There are reasons to buy the workstation NVidia cards over the consumer ones

I seem to recall certain esoteric OpenGL things like lines being fast was a NVIDIA marketing differentiator, as only certain CAD packages or similar cared about that. Is this still the case, or has that software segment moved on now?

[blagie]

I don't know the full set of differentiators.

For me (not quite at the A1000 level, but just above -- still in the prosumer price range), a major one is ECC.

Thermals and size are a bit better too, but I don't see that as $500 better. I actually don't see (m)any meaningful reasons to step up to an Ax000 series if you don't need ECC, but I'd love to hear otherwise.

[KeplerBoy]

"release from a year and a half ago", that's technically true but a really generous assessment of the situation.

We could just as well compare it to the slightly more capable RTX A2000, which was released more than 4 years ago. Either way, Intel is competing with the EoL Ampere architecture.

[tossandthrow]

... At a current day cheaper price.

There are huge markets that does not care about SOTA performance metrics but needs to get a job done.

[moffkalast]

> 1.7x the performance of the A1000 for AI inference

That's a bold claim when their acceleration software (IPEX) is barely maintained and incompatible with most inference stacks, and their Vulkan driver is far behind it in performance.