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Nvidia Announces PCIe version Tesla P100
Nvidia released the PCI-Express version of the P100 in the form of the Tesla P100. The hpc-card was revealed back in April already however was based on a Mezzanine-connector.
Nvidia is to release two versions of the PCI-Express models with 16GB HBM2 memory, and one with 12 GB HBMs (one stack disabled). The cards can communicate over the NVLINk interconnect with each other. The cards will get a slightly lower boost frequency compared to the Mezzanine NVLINK version, the TDP is lower as well at 250 Watt.
| Nvidia Tesla | ||||||
|---|---|---|---|---|---|---|
| Tesla P100 (Mezzanine) |
Tesla P100 (16GB) |
Tesla P100 (12GB) |
Tesla M40 | |||
| Gpu | GP100 (610mm2) |
GP100 (610mm2) |
GP100 (610mm2) |
GM200 | ||
| Architecture | Pascal | Pascal | Pascal | Maxwell 2 | ||
| Core | 1328MHz | - | - | 948MHz | ||
| Streamp processors | 3584 | 3584 | 3584 | 3072 | ||
| Boost | 1480MHz | 1300MHz | 1300MHz | 1114MHz | ||
| Mem. | 1,4Gbit/s HBM2 | 1,4Gbit/s HBM2 | 1,4Gbit/s HBM2 | 6Gbit/s gddr5 | ||
| Mem bus | 4096-bit | 4096-bit | 3072-bit | 384-bit | ||
| Mem bandwidth | 720GB/sec | 720GB/sec | 540GB/sec | 288GB/sec | ||
| Mem MGB | 16GB | 16GB | 12GB | 12GB | ||
| Half Precision | 21,2 tflops | 18,7 tflops | 18,7 tflops | 6,8 tflops | ||
| Single Precision | 10,6 tflops | 9,3 tflops | 9,3 tflops | 6,8 tflops | ||
| Double Precision | 5,3 tflops (1/2 rate) |
4,7 tflops (1/2 rate) |
4,7 tflops (1/2 rate) |
213 gflops (1/32 rate) |
||
| Transistors | 15,3 Billion | 15,3 Billion | 15,3 Billion | 8 Billion | ||
| Tdp | 300W | 250W | 250W | 250W | ||
| Formfactor | Mezzanine | pci-e | pci-e | pci-e | ||
| Cooler | N/A | passive | passive | passive | ||
| Fab | tsmc 16nm finfet | tsmc 16nm finfet | tsmc 16nm finfet | tsmc 28nm | ||
The 12GB version thus will have a 3072 bits wide memory bus, the 16GB version has the full 4096 bit wide memory bus. These cards are intended for high performance computing of course.
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Nvidia just announced a HPC data-center module based on Pascal GPU architecture. The Tesla P100 is a daughter add-in board that holds 150B transistors, the GPU itself however will hold 15B transistors...
NVIDIA announces new bundle with Tom Clancy The Division - 02/17/2016 03:59 PM
Mentioned a few days ago already, now confirmed. From today, 17 February, gamers who purchase a GeForce GTX 970 or above GPU or a notebook with a GeForce GTX 970M or above will receive a free copy of...
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Though I’m not quite sure if I’d be using Ashes of Singularity for testing this myself, it is an interesting read. Anandtech posted some benchmark testing a feature in DirectX 12 c...
Denial
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#5292647 Posted on: 06/20/2016 02:23 PM
I wish they had a vendor list of companies that are buying this, for my own curiosity. I know Google has their own Tensor Processing Unit which they claim is the best in the industry for deep learning. I wonder how these stack up.
I wish they had a vendor list of companies that are buying this, for my own curiosity. I know Google has their own Tensor Processing Unit which they claim is the best in the industry for deep learning. I wonder how these stack up.
Cave Waverider
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Posts: 1353
Joined: 2010-07-25
#5292661 Posted on: 06/20/2016 03:14 PM
I assume we could see something similar as the PCIe versions for the Geforce Ti and Titan Pascal variants, respectively.
I assume we could see something similar as the PCIe versions for the Geforce Ti and Titan Pascal variants, respectively.
BD2015
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Posts: 51
Joined: 2015-08-08
#5292662 Posted on: 06/20/2016 03:21 PM
Exactly what I hope for and can't wait to.
I assume we could see something similar for the Geforce Ti and Titan Pascal variants, respectively.
Exactly what I hope for and can't wait to.
schmidtbag
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Posts: 6570
Joined: 2012-11-10
#5292671 Posted on: 06/20/2016 03:45 PM
I'm not sure I understand. Teslas are as similar to TPUs as they are to CPUs. Teslas are meant for high-precision highly-parallel number crunching. The TPUs, to my knowledge, are meant for rapid approximations.
In the server world, you buy what does your workload fastest, and that's why architectures like PPC, SPARC, and AMD's Bulldozer are still relevant. Intel is really the only company that has any interest in general-purpose servers.
I wish they had a vendor list of companies that are buying this, for my own curiosity. I know Google has their own Tensor Processing Unit which they claim is the best in the industry for deep learning. I wonder how these stack up.
I'm not sure I understand. Teslas are as similar to TPUs as they are to CPUs. Teslas are meant for high-precision highly-parallel number crunching. The TPUs, to my knowledge, are meant for rapid approximations.
In the server world, you buy what does your workload fastest, and that's why architectures like PPC, SPARC, and AMD's Bulldozer are still relevant. Intel is really the only company that has any interest in general-purpose servers.
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Check spelling^