|
gptkbp:instance_of
|
gptkb:microprocessor
|
|
gptkbp:architecture
|
gptkb:x86
gptkb:ARM
|
|
gptkbp:benefits
|
Efficiency
Flexibility
Scalability
Cost-effectiveness
|
|
gptkbp:case_analysis
|
gptkb:Autonomous_Vehicles
Climate Modeling
Financial Modeling
Genomic Sequencing
COVID-19 Research
|
|
gptkbp:challenges
|
Power Consumption
Initial Cost
Cooling Requirements
Complexity of Setup
|
|
gptkbp:collaborates_with
|
Research Institutions
Universities
|
|
gptkbp:developed_by
|
gptkb:NVIDIA
|
|
gptkbp:features
|
Parallel Processing
GPU Acceleration
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
NVIDIA Supercomputers
|
|
gptkbp:market_segment
|
gptkb:enterprise_solutions
gptkb:government
gptkb:Education
Finance
Healthcare
|
|
gptkbp:notable_examples
|
gptkb:NVIDIA_Cambridge-1
gptkb:NVIDIA_Selene
gptkb:NVIDIA_DGX_Super_POD
|
|
gptkbp:operating_system
|
gptkb:Linux
|
|
gptkbp:performance
|
gptkb:FLOPS
Latency
Throughput
|
|
gptkbp:supports
|
gptkb:simulation
gptkb:Data_Science
Model Training
|
|
gptkbp:trends
|
gptkb:Edge_Computing
Sustainability Initiatives
Increased AI Capabilities
Quantum Computing Integration
|
|
gptkbp:used_for
|
gptkb:Artificial_Intelligence
gptkb:Deep_Learning
High Performance Computing
|
|
gptkbp:uses
|
gptkb:Tensor_RT
gptkb:NVIDIA_HPC_SDK
gptkb:CUDA
|
|
gptkbp:bfsParent
|
gptkb:A100_Tensor_Core_GPU
|
|
gptkbp:bfsLayer
|
5
|