Olona-Apeere GPU Partitioning adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Which resource boundary distinguishes separate MIG GPU Instances from software time slicing?
   • Only a different process priority on one shared execution queue
   • A guaranteed absence of every host or device failure
   • A separate network cable for every CUDA kernel
   • Dedicated hardware compute and memory resources for each GPU Instance
2. Lori kini faaji NVIDIA ti ṣe afihan MIG akọkọ?
   • Volta
   • Ampere (A100)
   • Turing
   • Pascal
3. On an A100 40 GB using supported small-instance profiles, how many GPU Instances can a layout provide?
   • Exactly sixteen on every supported GPU
   • Exactly two regardless of profile
   • Up to seven
   • An unlimited number with no memory limits
4. Ninu profaili MIG kan bi '1g.5gb', kini '5gb' ṣe aṣoju?
   • Iranti ti a pin si apẹẹrẹ yẹn
   • Nọmba ti SMS
   • Awọn bandiwidi PCIe
   • Iyara aago
5. Iru iṣẹ wo ni MIG paapaa dara julọ fun?
   • Ṣiṣe ikẹkọ nla kan ṣoṣo ti o nilo gbogbo GPU
   • Itọkasi agbatọju pupọ ti n ṣiṣẹ pẹlu airi asọtẹlẹ
   • Sipiyu-owun data preprocessing
   • Ibi ipamọ otutu igba pipẹ
6. Bawo ni iṣupọ Kubernetes ṣe le ṣafihan awọn iṣẹlẹ MIG si awọn adarọ-ese?
   • Bi aise disk iwọn didun
   • Nikan nipasẹ SSH afọwọṣe
   • Gẹgẹbi awọn orisun iṣeto bi 'nvidia.com/mig-1g.5gb'
   • Nipasẹ awọn igbasilẹ DNS
7. Two Compute Instances belong to the same MIG GPU Instance. Which resources do they share?
   • Their parent GPU Instance’s memory resources
   • Only a remote CPU’s filesystem
   • Separate complete copies of physical GPU memory
   • Every compute slice on unrelated GPUs
8. A team wants to deploy a named MIG profile on a new GPU model. What should it check first?
   • That every NVIDIA GPU supports identical profiles
   • That the model parameter count is an even integer
   • That the Kubernetes pod name contains the profile text
   • That the GPU supports MIG and that exact profile layout