NVIDIA's Volta architecture
GPTKB entity
Statements (84)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:bfsLayer |
6
|
| gptkbp:bfsParent |
gptkb:NVIDIA's_Pascal_architecture
|
| gptkbp:based_on |
G P100 GPU
|
| gptkbp:competes_with |
gptkb:AMD_Vega_architecture
|
| gptkbp:developed_by |
gptkb:DJ
|
| gptkbp:enables |
real-time ray tracing
|
| gptkbp:features |
gptkb:Tensor_Cores
|
| https://www.w3.org/2000/01/rdf-schema#label |
NVIDIA's Volta architecture
|
| gptkbp:improves |
deep learning performance
|
| gptkbp:includes |
NV Link
|
| gptkbp:is_a |
gptkb:architect
|
| gptkbp:is_associated_with |
gptkb:NVIDIA_Deep_Learning_SDK
|
| gptkbp:is_compatible_with |
various operating systems
NVIDIACUDA Toolkit |
| gptkbp:is_designed_for |
high-performance computing
|
| gptkbp:is_designed_to |
handle large datasets
accelerate scientific research accelerate AI training support AI inference |
| gptkbp:is_evaluated_by |
its reliability
its scalability its innovative design performance in simulations |
| gptkbp:is_featured_in |
industry publications
supercomputers AI research papers technology conferences |
| gptkbp:is_integrated_with |
cloud computing environments
AI development tools AI training platforms NVIDIARTX series |
| gptkbp:is_known_for |
high throughput
its versatility high memory capacity high compute performance improve model accuracy reduce training time enhance computational efficiency its high performance per watt |
| gptkbp:is_optimized_for |
gptkb:software_framework
|
| gptkbp:is_part_of |
NVIDIA's data center solutions
NVIDIA's AI ecosystem NVIDIA's AI research initiatives NVIDIA's hardware lineup NVIDIAGPU family |
| gptkbp:is_recognized_by |
a breakthrough in GPU technology
a game changer in AI technology a leader in GPU performance |
| gptkbp:is_recognized_for |
its architectural innovations
AI research advancements enhanced graphics rendering its impact on technology advancements |
| gptkbp:is_supported_by |
NVIDIA drivers
|
| gptkbp:is_used_for |
image processing
natural language processing scientific computing deep learning frameworks |
| gptkbp:is_used_in |
financial modeling
virtual reality applications robotics applications NVIDIADGX systems |
| gptkbp:is_utilized_in |
gptkb:engine
data analysis research institutions big data analytics academic researchers machine learning frameworks machine learning practitioners computer vision tasks |
| gptkbp:manufacturer |
12 nm process
|
| gptkbp:offers |
better energy efficiency
|
| gptkbp:predecessor |
gptkb:NVIDIA's_Pascal_architecture
|
| gptkbp:provides |
enhanced memory bandwidth
|
| gptkbp:receives_funding_from |
cloud service providers
|
| gptkbp:released_in |
gptkb:2017
|
| gptkbp:successor |
gptkb:NVIDIA's_Turing_architecture
|
| gptkbp:supports |
gptkb:CUDA
multi-GPU configurations AI workloads |
| gptkbp:targets |
data centers
|
| gptkbp:used_in |
gptkb:NVIDIA_Tesla_V100
gptkb:NVIDIA_Titan_V |
| gptkbp:utilizes |
HB M2 memory
|