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This will allow 3 to physically fit onto this motherboard. The FTW3 edition was chosen because it is a true 2-slot card (not 2.5) with better cooling than the Founder’s Edition 1080Ti.
MULTI GPU WORKSTATION BUILD UPGRADE
As I am later in the upgrade cycle, I’ll upgrade to the 16GB Volta and resell my 1080Ti in the future – I anticipate only taking a loss of $2Ti on resell. Conversely, 12GB Pascal to 11GB Pascal is a relative lesser performance hit. A 16GB DDR5 Volta processor would be a significant performance gain from a 12GB Pascal for deep learning. As the PCIe bus is the bottleneck, and will remain so for a few years, batch size into DDR5 memory & CUDA cores will be where performance is gained. Also, at time of purchase, Volta architecture was announced. You can buy two 1080Ti’s for the price of one Titan Xp. The 1080Ti currently wins on price/performance. The 1080Ti differs in its memory architecture – 11GB DDR5 and a slightly slower, slightly narrower bandwidth vs. However, by spring 2017 there were two choices: The Titan Xp, with slightly faster memory speed & internal bus, and 256 more CUDA cores and the 1080Ti, the prosumer enthusiast version of the Titan X Pascal, with 3584 cores. Last year, choosing a GPU was easy – the Titan X Pascal, a 12GB 3584 CUDA-core monster. NVidia GeForce 1080Ti 11GB – EVGA FTW3 edition Retail: $800 A stable overclock to 4.0Ghz is easily achievable on the 6850K. Avoid the $650 6800K – pricier and slower with less (28) lanes.
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The 6950X is $1200 more for 4 extra cores, unnecessary for our purposes. $359 discounted is attractive compared to the 6900K, reviewed to offer minimal to no improvement at a $600 price premium. Socket LGA2011-v3 on the motherboard guides the CPU choice – the sweet spot in the Broadwell-E lineup is the overclockable 3.6Ghz 6850K with 6 cores and 15MB of 元 cache, permitting 40 PCIe lanes. Intel Core i7 6850K Broadwell-E CPU Socket Retail $649 The previous versions of ASUS X99 WS have been well reviewed.
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It also has a 10G Ethernet jack somewhat future-proofing it as I anticipate using large datasets in the Terabyte size. The PCIe 3.0-CPU lanes are the largest bottleneck in the system, so making these 16X helps the most.
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The board implements 40 PCIe 3.0 lanes which will support three 16X PCIe 3.0 cards (i.e. Therefore, I chose the ASUS X99 motherboard. Also, both architectures remain PCIe 3.0 at this time. While newer Intel Skylake and Kaby Lake CPU architectures & chipsets beckon, reliability is important in a computationally intensive build, and their documented complex computation freeze bug makes me uneasy. Retail $699Ī Motherboard sets the capabilities and configuration of your system.
MULTI GPU WORKSTATION BUILD WINDOWS 10
Dual Boot Windows 10 Pro & Ubuntu 16.04LTS.Stable & Reliable – minimize hardware bugs.Current ‘Best’ NVidia GPU with large DDR5 memory.I could have ended up with a multi-thousand dollar doorstop – fortunately, I did not. Since a DGX-1 was out of the question ($129,000), I decided to follow other pioneers building their own deep learning workstations. My venerable Xeon W3550 8GB T3500 running a 2GB Quadro 600 was outdated. With Tensorflow released to the public, the NVidia Pascal Titan X GPU, along with (relatively) cheap storage and memory, the time was right to take the leap from CPU-based computing to GPU accelerated machine learning. For machine learning and AI issues, please visit the new site!