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Anandtech have coverage of the SIGGRAPH keynote at the link below. Headline specs for Turing: 754mm^2 die area 18.6 billion transistors up to 16TFLOPs up t
by jdietrich 8y ago
Anandtech have coverage of the SIGGRAPH keynote at the link below.
Headline specs for Turing:
754mm^2 die area
18.6 billion transistors
up to 16TFLOPs
up to 500 trillion tensor ops per second
It looks like an absolute beast, but don't expect to see anything remotely like it at a consumer price point.
https://www.anandtech.com/show/13215/nvidia-siggraph-2018-keynote-live-blog-4pm-pacific https://www.anandtech.com/show/13215/nvidia-siggraph-2018-ke...
- abkumar 8y agoPricing [1] since the anandtech live blog doesn't list it. * Quadro RTX 8000 with 48GB memory: $10,000 estimated street price * Quadro RTX 6000 with 24GB memory: $6,300 ESP * Quadro RTX 5000 with 16GB memory: $2,300 ESP [1] https://nvidianews.nvidia.com/news/nvidia-unveils-quadro-rtx-worlds-first-ray-tracing-gpu https://nvidianews.nvidia.com/news/nvidia-unveils-quadro-rtx...
- TheRealRiane 8y ago$10,000? They'll have people buy the whole lot anyways. Buy NVDA
- samplatt 8y agoYes, they will. Not even counting cryptomining, universities and big-businesses use NVidia-powered supercomputers with 5000+ $10k cards in them, for AI deep learning or for astrophysical modelling or other things. Previously those cards have been the ~$8k-$10k P100 or V100. Not any more.
- girvo 8y agoInterestingly, I game on a P1something, through AWS and parsecgaming.com — very cool tech, and actually worthwhile for me price wise as I don’t game often enough to invest a couple grand in a gaming PC.
- Cthulhu_ 8y agoAWS and GCE as well, they can put them in their datacenters and run them 24/7, with their customers either renting it for a few minutes to hours for the odd task, researchers for hours / days to run a huge task, and the spot market making sure they never go unused. For them it's more of a matter of how much they're used in terms of % of time, and how much they can charge per compute hour. They can earn the investment back within a year.
- gspetr 8y agoI know meta comments are unwelcome but "nvidianews.nvidia.com/news/" is so egregious that it caught my eye. It looks like their entire website is a huge legacy mess. "news.nvidia.com" leads nowhere. "nvidia.com/news/" leads to "nvidia.com/content/redirects/news.asp" and produces "Corrupted Content Error" The page you are trying to view cannot be shown because an error in the data transmission was detected.
- TheSpiceIsLife 8y agoI suspect probably something like: spending money on website appears to do nothing to increase sales.
- archagon 8y agoAlas, there's nothing at https://nvidianews.nvidia.com/news/new https://nvidianews.nvidia.com/news/new.
- jadbox 8y agoThat's kind of amazing... also impressed you took the time to determine this.
- samstave 8y ago> Quadro RTX 5000 with 16GB memory: $2,300 ESP* For reference, I purchased my first 3D card from Evan's and Sutherland to run Softimage with 32MB Ram in ~1996 for $1,800.
- brodsky 8y agoi haven't done the math, but the 1996 $1800 seems like more money than 2018 $2300.
- bri3d 8y agoI found an old Computer Shopper from the late 1990s and had forgotten how ridiculously expensive computer equipment was - the real sub-$1000 market wasn't even a thing for PCs until 1997, and the range between a sub-$1000 computer and an expensive one was astounding even for day-to-day tasks.
- pjmlp 8y agoMy 386SX 20 MHz, 2MB RAM, 40 MB HDD bought in 1991, was about 1 500 euros in today's money.
- samstave 8y ago>the real sub-$1000 market wasn't even a thing for PCs until 1997 YES! I was at intel when they were doing the initial research to even see if a <$1,000 machine was feasible. With the celeron... and this is when they were literally paying millions to companies to optimize to the intel processors so they had software that would be subjectively digestible by the market to purchase software and machines thinking that they were getting compute power for their buck. /lawn.
- tanilama 8y agortx 5000 is actually not too expensive as comparing to the launch of Volta series.
- gammateam 8y agoI understand the technical novelty, but from a financial perspective for the NVIDIA company and the customers and data centers they pop these things in: is this a big deal? Is this just routine new cycle stuff that amazon and microsoft are going to buy en masse like they already would have no matter what Nvidia came out with? Or is this a big deal for reasons that you will explain and the stock market is going to go wild attempting to "price this in"?
- mozumder 8y agoDie size is ridiculous, causing such high prices due to exponential costs associated with die size. It seems like an opening for AMD to use their EPYC Infinity Fabric interconnect for their next-gen GPUs in multi-chip modules with smaller dies.
- jdietrich 8y agoIt's smaller than Volta, but that isn't saying much - as far as I can tell, Volta is the biggest commercially-produced processor die ever. https://arstechnica.com/gadgets/2017/05/nvidia-tesla-v100-gpu-details/ https://arstechnica.com/gadgets/2017/05/nvidia-tesla-v100-gp...
- tasty_freeze 8y agoDie prices rise exponentially with area if your product requires a flawless die. But in architectures where bad subsections can get mapped out, such as GPUs and multicore CPUs, the price rise is not exponential.
- TomVDB 8y agoKepler GPU die sizes were pretty small. Maxwell die sizes on the same process were much larger. (Up to 600mm2.) Pascal die sizes were pretty small. The Turing die size is much larger on (essentially) the same process. You’re seeing the power of improving yields as the process matures. It’s likely that a 7nm chip with the same functionality as this one would cost more for the foreseeable future, so it makes sense to make it this big. The BW of the infinity fabric is but a fraction of what’s needed for a GPU, so that’s a total non-starter.
- mlindner 8y ago754mm^2 = 1.17 square inches. That's a die size that's bigger than a postage stamp.
- mrb 8y ago«don't expect to see anything remotely like it at a consumer price point.» Strictly based on the perf metrics you listed, AMD already sells something close (matching TFLOPS within 15%), and at a consumer price point: AMD Radeon RX Vega 64 Liquid ($699): • 486 mm² • 12.5 billion transistors • up to 13.7 TFLOPS (single precision, which is probably what Nvidia is quoting, although I'd like to see confirmation) • no tensor ALU though
- boobsbr 8y agoI haven't kept up with GPUs since a long time ago. I remember reading that even though Quadro GPUs were for heavy workstation loads, it still didn't compare to consumer GPUs when used for playing games. How would this Quadro RTX compare to a Titan GTX 1080 Ti for games with standard rendering techniques?
- FooHentai 8y ago> I remember reading that even though Quadro GPUs were for heavy workstation loads, it still didn't compare to consumer GPUs when used for playing games. Different focus. Gaming GPUs are tweaked for speed. Workstation GPUs are tweaked for precision. Some other considerations like compatibility, thermal/power efficiency, and duty cycles also factor in. This was borne out more obviously ~10 years back when you could soft-mod some nvidia gaming GPUs to be quadros, turning a $200 gaming GPU into a $600 workstation card just by melting a hardware fuse. Today, the cards are probably too complicated to allow for that, even if you do find a way of tricking the drivers.