Nvidia Reveals RTX 6000 with 48GB GDDR6 ECC Memory
tomshardware.com
tomshardware.com
[1]https://www.nvidia.com/content/dam/en-zz/Solutions/design-vi...
Or since Nvidia is historically stupid with naming systems and can't stick with them, maybe the RTX 4-digit numbering goes away before 6000 series.
They had a chance to keep a numbering system going when they did the GTX 1xx, GTX 2xx, GTX 3xx.... GTX 9xx, GTX 10xx, and could have gone to GTX 11xx and been set for as long as they wanted (well over a hundred years) counting up toward GTX 99xx), but instead they went to GTX 20xx and RTX 30xx so now they're going to run out of numbers again.
Bets on what's next? Do we hit RTX 10,000 and then jump back down to XTX 100?
Numbers don’t lie people, stop fucking with it. We’re up to the GeForce 20 basically. Marketing is a helluva a drug.
Few examples: there was GeForce MX 4000 (in 2003), GeForce GTX 480 (in 2010) and now GeForce RTX 4080.
Intel at least had the decency to go into five figure numbers with their CPUs.
IMO it’s shrinkflation for GPUs and the naming confusion is used as an attempt to hide it. My prediction is they change the entire naming scheme for the next generation because the current scheme invites historical comparison and criticism like the Reddit post I linked.
Watch them switch to pure, meaningless names. It wouldn’t surprise me.
1. https://old.reddit.com/r/pcmasterrace/comments/xk2nsf/the_40...
G-M, where G is the generation and M is the market segment. They could even make M a letter (A, B, C) to avoid confusion (rather than using 50, 60, 70, 80, which was the closest thing they got to a convention). A=awesome, B=better, C=consumer. They can stick a W on the front for workstation cards, and give them another letter at the end for special high-memory runs or whatever.
So this could be something like the: w20a-h.
Is that really a thing? That strikes me as the same level of shady practices as things like Planned Obsolescence.
[1]https://www.nvidia.com/en-us/geforce/graphics-cards/40-serie... (you'll have to manually scroll down and click the Specs tab)
"F150 V8": an expensive truck with a V8 motor.
"F150 V6" a cheaper V6 sedan.
Clearly, these are two different products, even though they can do the same thing. You're sneakily using a known naming convention in your official product name to deceive customers on the price perception of the 4080 and the performance of the 4080 12GB.
Maybe the EU can whip them into shape.
They stopped giving a shit. They want you to upgrade to the next series ala Apple.
There's a "renaissance" of this bullshit in the entertainment space, too, where apparently these loons think that resetting the numbers is cool. For example, "Doom" (2016) instead of the more sensible "Doom 4", because fuck that game that came out in 1994!
Or how Nvidia simply reset all their product numbers a while ago. My first graphics card was a geforce TI 4200! And now they are back to the 4000s!
The NVidia thing is a reflection of them having a hard time segmenting the market. The old “enterprisy engineers” vs “gamers” hasn’t worked for a long time, and they feel they deserve a bigger piece of the action on the server/cloud side.
Get a few large channels on board and hopefully they won't try it again
Unfortunately, it is probably also a way to get your early review sample privilege revoked.
Maybe NVIDIA will help people slowly start to realize that employees should not have to play the telephone game to make purchases.
Yes, it’s a quadro card.
Big fuck you for nvidia with this release prosumer market is dying right now and there is no alternative cause AMD software is so bad you are forced to use CUDA in for example DL/DS.
You reap what you sow.
On a side note, AMD did a different (yet interesting) move by embracing the FOSS culture (working with uptreams).
I remember writing CUDA parallelized C++ code in 2010 and it was a piece of cake.
Has Nvidia taken advantage of their position? Absolutely. But don't fault the ML community when there was nothing else available.
OpenCL has existed for about as long as CUDA, and can be used on GPUs from any of the major manufacturers. What makes OpenCL so unsuitable for ML that the ML community just had to use CUDA?
Now that AMD has money, hopefully they have fixed their stack, but I'm still in "once bitten, twice shy" mode. I want to see someone else in my field using AMD on tasks I care about before I try team red again.
In contrast, CUDA compiles down to a device agnostic intermediate language so they can support the same code on several generations of hardware, limited only by the CUDA feature level available on the hardware.
AMD still has a long way to go for now. Both in terms of 'primary' functionality like supported hardware and compilers and 'secondary' functionality like detailed profiling and debugging tools.
NVIDIA also partnered up very quickly with many big players in the game, the sales people went to work, but they had the technological feats to back it up.
After that, it's the network effect.
Nope, AMD never gave them a choice.
They may be hoping to live off gamers and big server contracts (eg Frontier) until they get their software stack in a decent position and outgrow their current reputation of having buggy software.
Not even counting Eastern Europe and Asia where you get fraction of that.
Then again, I've been places that are stingy about printing, which didn't make much sense to me, so I guess they have some... other way of looking at this stuff.
Here's the full table pasted:
GPU Engine Specs: NVIDIA CUDA® Cores 16384 Boost Clock (GHz) 2.52 Base Clock (GHz) 2.23
Memory Specs: Standard Memory Config 24 GB GDDR6X Memory Interface Width 384-bit
Technology Support: Ray Tracing Cores 3rd Generation Tensor Cores 4th Generation NVIDIA Architecture Ada Lovelace NVIDIA DLSS 3 NVIDIA Reflex Yes NVIDIA Broadcast Yes PCI Express Gen 4 Yes Resizable BAR Yes NVIDIA® GeForce Experience™ Yes NVIDIA Ansel Yes NVIDIA FreeStyle Yes NVIDIA ShadowPlay Yes NVIDIA Highlights Yes NVIDIA G-SYNC® Yes Game Ready Drivers Yes NVIDIA Studio Drivers Yes NVIDIA Omniverse Yes Microsoft DirectX® 12 Ultimate Yes NVIDIA GPU Boost™ Yes NVIDIA NVLink™ (SLI-Ready) No Vulkan RT API, OpenGL 4.6 Yes NVIDIA Encoder (NVENC) 2x 8th Generation NVIDIA Decoder (NVDEC) 5th Generation AV1 Encode Yes AV1 Decode Yes CUDA Capability 8.9 VR Ready Yes
Display Support: Maximum Digital Resolution (1) 7680x4320 Standard Display Connectors HDMI(2), 3x DisplayPort(3) Multi Monitor 4 HDCP 2.3
Card Dimensions: Length 304 mm Width 137 mm Slots 3-Slot (61mm)
Thermal and Power Specs: Maximum GPU Temperature (in C) 90 Graphics Card Power (W) 450 W Minimum System Power (W) (4) 850 W Supplementary Power Connectors 3x PCIe 8-pin cables (adapter in box) OR 450 W or greater PCIe Gen 5 cable
Personally, I would buy a couple of 3090's with NVLink and call it day if it was for personal use. I would wait and see actual performance before even thinking about Lovelace cards. Nvidia makes all kinds of unsubstantiated claims during these events.
This is not an oversight on NVIDIA's part, this is intentional product segmentation.
You could buy 2 x 3090's with NVLINK for half that. I recently bought a datacenter M40 with 24GB of RAM for $150 on Ebay then added water cooling. The Dell Precision 5810 I bought second hand can now run some really interesting stuff on those 24GB of RAM albeit slower than a 3090. For less than $500 I can have two watercooled M40's that will run almost anything I can throw at them.
None of major ML frameworks such as Pytorch of TF support that. I’m not sure why.
NVLink is important if (1) if your model doesn't fit in the RAM on a single card or (2) you want to increase batch size past the point of each batch fitting in the RAM of a single card or (3) to increase training speed by spreading the workload.
https://www.nvidia.com/en-us/design-visualization/rtx-a6000/
Serious question.
They have a RTX 4090, RTX 4080, and RTX 4080 but less good. And now RTX 6000?
What happens in 2 generations when they want to make a RTX 6090 and so on? Why are there 2 very different RTX 4080?
I mean, really, this started at the 3000 series. I have a laptop with a dock that contains a RTX 3080, full stop. But you have to dig into the specs to learn it's not a full RTX 3080. But it's also not a RTX 3080 Max Q? It's a "RTX 3080 Laptop" whatever that's supposed to mean. But that's not in the marketing. It's fully implying that it's a full RTX 3080.
This is how users get confused and angry when their hardware isn't working as expected.
So the chances are pretty high this would go into a machine with ECC RAM for the system as well.
At least on the workstation side - i do a lot of solidworks-based 3d modeling. The A6000 can easily, for example, raytrace in real time anything i can even find to throw at it. Like models that Solidworks still has trouble opening and rendering normally can raytrace instantly.
What part of workstation software is still GPU bound at this point on high end GPU's?
I guess if CAM was taking more advantage of GPU's, i could see it useful there, but on the modeling side, i honestly don't get it.
I’ve done color correction in games and it’s computationally very cheap to throw a LUT in there, so clearly something else is going on?
In a full color grading session, you can have power windows. You can have those power windows tracking. You can have noise reduction. You can have other filters. You can do so so much. All of that takes up memory. You can be dealing with 8k footage. You can have many many many layers of that footage. You can be grading in >16bit color space.
You can do all of that with very little VRAM at a snails pace, or you can do it in real-time with all of that VRAM and keep the client in a supervised session happy. With all of that VRAM, you can export at faster than real-time for delivering the content to the client at the end of the session rather than the next day after an overnight render.
That's working on Hero assets for current-tech games. UE5 Nanite assets and film assets are much heavier.
They have proven to be the best at this discipline with no competitor in sight.
Consumers of the world are in awe.
It's part of their ML profit strategy.
There's really nothing you can do outside of soldering on higher capacity RAM chips onto a 4090. There are some 20GB 3080 cards floating around out there.
I guess even for $6k on a graphics card you still can't effectively future proof anymore.
Which confuses me, is this one of the 4080 chips with a lot of memory?
In RTX 4090, 2k of the 18k are disabled, so only 16k are available.
In RTX 6000, presumably both the clock frequency and the supply voltage are lower, for better reliability, and that lowers the power consumption from 450 W to 300 W.
Catch a grip nvidia - this is a joke.