NCA Data Center GPU Solutions 5 — Questions and Answers
Question 1: A financial services company requires GPU acceleration for risk modeling but cannot move data to public cloud. Which NVIDIA solution enables on-premises GPU cloud infrastructure?
- NVIDIA AI Enterprise on public cloud only
- NVIDIA DGX BasePOD reference architecture with NVIDIA AI Enterprise (Correct answer)
- NVIDIA Fleet Command (cloud-only SaaS)
- NVIDIA Omniverse Enterprise
Correct answer: NVIDIA DGX BasePOD reference architecture with NVIDIA AI Enterprise
NVIDIA DGX BasePOD provides a validated on-premises reference architecture with DGX systems, networking, and storage, paired with NVIDIA AI Enterprise software for private cloud AI.
Question 2: What does the NVIDIA Grace CPU Superchip paired with H100 (Grace Hopper Superchip) achieve that a standard PCIe H100 cannot?
- Higher GPU clock speeds due to reduced thermal throttling
- A unified high-bandwidth memory architecture connecting CPU and GPU via NVLink-C2C at 900 GB/s (Correct answer)
- Direct connection to InfiniBand fabric without a separate HCA
- Support for more MIG instances per GPU than standard H100
Correct answer: A unified high-bandwidth memory architecture connecting CPU and GPU via NVLink-C2C at 900 GB/s
The Grace Hopper Superchip connects the NVIDIA Grace ARM CPU and H100 GPU via NVLink-C2C, providing 900 GB/s coherent interconnect bandwidth versus ~64 GB/s for PCIe Gen 5.
Question 3: Which NVIDIA collective communication library is used to optimize multi-GPU and multi-node all-reduce operations in distributed deep learning training?
- cuBLAS
- NCCL (NVIDIA Collective Communications Library) (Correct answer)
- cuDNN
- DALI
Correct answer: NCCL (NVIDIA Collective Communications Library)
NCCL provides optimized implementations of collective operations like all-reduce and all-gather, automatically using NVLink or InfiniBand for the fastest available path.
Question 4: A data center architect is evaluating GPU solutions for a genomics pipeline requiring high FP64 (double precision) performance. Which NVIDIA GPU is most suitable?
- GeForce RTX 4090
- NVIDIA L40S
- NVIDIA A100 or H100 SXM (Correct answer)
- NVIDIA T4
Correct answer: NVIDIA A100 or H100 SXM
A100 and H100 SXM variants deliver full FP64 performance (9.7 and 33.5 TFLOPS respectively), whereas consumer and some professional GPUs have severely reduced FP64 rates.
Question 5: What is the purpose of NVIDIA's GPUDirect Storage technology in a data center AI pipeline?
- It compresses GPU framebuffer data before writing to NVMe storage
- It enables direct data transfers between NVMe/NFS storage and GPU memory, bypassing the CPU and system RAM (Correct answer)
- It replicates GPU memory contents to NVMe for fault tolerance
- It allows GPUs to boot directly from NVMe without a host CPU
Correct answer: It enables direct data transfers between NVMe/NFS storage and GPU memory, bypassing the CPU and system RAM
GPUDirect Storage creates a direct path from storage (NVMe, NFS) to GPU memory, reducing CPU overhead and system memory bottlenecks during large dataset I/O.
Question 6: In NVIDIA's MIG (Multi-Instance GPU) feature on A100, which resource is NOT independently partitioned per MIG instance?
- Streaming Multiprocessors (SMs)
- L2 cache slices and HBM memory
- NVLink bandwidth between GPU instances (Correct answer)
- Memory bandwidth allocation
Correct answer: NVLink bandwidth between GPU instances
NVLink connects the entire GPU to external devices and is not partitioned between MIG instances; each instance gets its own SMs, L2 slices, memory, and bandwidth.
Question 7: Which NVIDIA software platform provides a model-serving framework that supports dynamic batching, model ensembles, and multi-GPU inference for production deployments?
- NVIDIA Triton Inference Server (Correct answer)
- NVIDIA TensorFlow Serving Fork
- NVIDIA RAPIDS cuML Server
- NVIDIA DeepStream SDK
Correct answer: NVIDIA Triton Inference Server
Triton Inference Server supports multiple frameworks (TensorRT, ONNX, PyTorch, TensorFlow), dynamic batching, model pipelines, and scales across multiple GPUs and nodes.
NCA Data Center GPU Solutions 5
A financial services company requires GPU acceleration for risk modeling but cannot move data to public cloud.
Which NVIDIA solution enables on-premises GPU cloud infrastructure?