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AIDA64 adds preliminary support for GV104 and GV104M and Hardware IDs
Diagnostic software often is a good source for finding entries, that really never should have been listed. This round it is AIDA that kinda confirms the new Geforce are coming, however, it doesn't mean the author had access to the GPU(s) most likely just device/PCI ID and added them as register in AIDA64.
This is still a drop in the ocean because we need NVIDIA to announce the release date and because people don't believe it until they do. AIDA, however, is listing the GV102, GV102GL, GV104, GV104M, and when you extract and snapshot the pci-ids, you will run into Device ID 1e87 GV104 being listed as GeForce GTX 1180. This is now the second tool adding the entry GV104. AIDA however has a name tagged to it, and that GV is short for GeForce Volta.
Version: 5.97.4679 beta (Aug 06, 2018).
Release notes:
- Hardware Monitoring / new items: CPU1 Package, CPU2 Package, CPU3 Package, CPU4 Package temperatures
- Hardware Monitoring / new items: CPU PLL2, PROM Core, PROM PHY voltages
- NVMe SSD temperature is now the highest temperature sensor reading
- sensor support for Dell SMI of OptiPlex 7060, Precision 7720, Precision 7730, Vostro 3670
- motherboard specific sensor info for Asus Prime H310M-E/BR, ROG Strix B450 Series
- motherboard specific sensor info for Gigabyte B450 Series
- motherboard specific sensor info for MSI MS-7B92
- improved motherboard specific sensor info for ASRock boards
- GPU information for nVIDIA CMP 100-100 (GP100)
- GPU information for nVIDIA CMP 100-200 (GV100)
- GPU information for nVIDIA CMP 100-210 (GV100)
- GPU information for nVIDIA Tesla V100-DGXS-32GB (GV100GL)
- GPU information for nVIDIA Tesla V100-PCIE-32GB (GV100GL)
- GPU information for nVIDIA Tesla V100-SXM2-32GB (GV100GL)
- extended GPU information for Intel i740
- preliminary GPU information for NVIDIA GV102, GV102GL, GV104, GV104M
- identification of AMD A4-9xxx (aka Stoney Ridge)
- Intel Processor Number detection for Core i7-9700K
- Intel Processor Number detection for Core i9-9900K
- VIA Processor Number detection for Nano X2 C4350AL
- fixed: CPU diode temperature measurement for AMD Ryzen 2000 Series
- fixed: sensor support for Asetek VII (Corsair iCUE SiUSBXp.dll issue)
- fixed: RAID member enumeration for Intel NVMe RAID arrays
The IDs of the GPUs are listed, with an entry called GeForce GTX 1180 (thanks EL1TE!):
HardwareCaps
Senior Member
Posts: 452
Joined: 2018-05-03
Senior Member
Posts: 452
Joined: 2018-05-03
#5571642 Posted on: 08/07/2018 01:20 PM
We should expect the expected which should be support for Ray-Tracing, new GDDR6 memory and small optimizations and improvements.
nothing huge, anything more would be a surprise for me
We should expect the expected which should be support for Ray-Tracing, new GDDR6 memory and small optimizations and improvements.
nothing huge, anything more would be a surprise for me
Denial
Senior Member
Posts: 12833
Joined: 2004-05-16
Senior Member
Posts: 12833
Joined: 2004-05-16
#5571643 Posted on: 08/07/2018 01:27 PM
Tensor cores are optimized for AI workloads. are you suggesting it would be used in consumer graphics as well? Have you seen any support for the tensor core in game engines or games that could suggest that?
sounds like a waste of silicon for me. using that space for regular CUDA cores sounds like a better idea, maybe I'm wrong and Nvidia is planning to use tensor cores for its geforce series as well but it sounds very very fishy
Tensor cores are optimized for matrix operations - AI workloads utilize matrix operations but other things do as well. Also RTX is accelerated by Tensor - https://devblogs.nvidia.com/nvidia-optix-ray-tracing-powered-rtx/ but fwiw AMD has implemented it's own denoiser on mixed math FP16 - which next generation consumer Nvidia hardware might also get.
Tensor cores are optimized for AI workloads. are you suggesting it would be used in consumer graphics as well? Have you seen any support for the tensor core in game engines or games that could suggest that?
sounds like a waste of silicon for me. using that space for regular CUDA cores sounds like a better idea, maybe I'm wrong and Nvidia is planning to use tensor cores for its geforce series as well but it sounds very very fishy
Tensor cores are optimized for matrix operations - AI workloads utilize matrix operations but other things do as well. Also RTX is accelerated by Tensor - https://devblogs.nvidia.com/nvidia-optix-ray-tracing-powered-rtx/ but fwiw AMD has implemented it's own denoiser on mixed math FP16 - which next generation consumer Nvidia hardware might also get.
HardwareCaps
Senior Member
Posts: 452
Joined: 2018-05-03
Senior Member
Posts: 452
Joined: 2018-05-03
#5571644 Posted on: 08/07/2018 01:33 PM
I would be surprised to see tensor cores used for gaming maybe it is a refreshing architecture after all.
Tensor cores are optimized for matrix operations - AI workloads utilize matrix operations but other things do as well. Also RTX is accelerated by Tensor - https://devblogs.nvidia.com/nvidia-optix-ray-tracing-powered-rtx/ but fwiw AMD has implemented it's own denoiser on mixed math FP16 - which next generation consumer Nvidia hardware might also get.
I would be surprised to see tensor cores used for gaming maybe it is a refreshing architecture after all.
Noisiv
Senior Member
Posts: 6990
Joined: 2010-11-16
Senior Member
Posts: 6990
Joined: 2010-11-16
#5571653 Posted on: 08/07/2018 02:12 PM
Nvidia does not let anything go to waste.
Even though Volta will be 15 months old by the time 1180 debuts, they're still gonna (re)use Volta SM (they claim 50% more efficient than Pascal). A bit disappointing that they're saving themselves for 7nm, but what can you do...
It seems to me that the biggest challenge for Nvidia is not R&D, but how to use the existing IP to create the most optimum sequence of products and launches.
Nvidia does not let anything go to waste.
Even though Volta will be 15 months old by the time 1180 debuts, they're still gonna (re)use Volta SM (they claim 50% more efficient than Pascal). A bit disappointing that they're saving themselves for 7nm, but what can you do...
It seems to me that the biggest challenge for Nvidia is not R&D, but how to use the existing IP to create the most optimum sequence of products and launches.
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Senior Member
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Joined: 2018-05-03
Tensor cores are optimized for AI workloads. are you suggesting it would be used in consumer graphics as well? Have you seen any support for the tensor core in game engines or games that could suggest that?
sounds like a waste of silicon for me. using that space for regular CUDA cores sounds like a better idea, maybe I'm wrong and Nvidia is planning to use tensor cores for its geforce series as well but it sounds very very fishy