Guide-yakabatana mibvunzo · Pakati Nhanho

Multi-Instance GPU Partitioning Quiz

Nyorera MIG profiles, GPU-versus-compute muenzaniso zviwanikwa, uye kuratidzwa kwemudziyo, kusanganisira nei inotsigirwa hardware inotara sarudzo dzekuparadzanisa.

Nzira dzekutungamira dzinoenderanaMulti Instance GPU Partitioning
Mubvunzo 1 ye 8

Which resource boundary distinguishes separate MIG GPU Instances from software time slicing?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  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. Ndeipi NVIDIA yekuvakisa yakatanga kuunzwa MIG?
    • 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. Mune chimiro cheMIG se'1g.5gb', '5gb' inomiririrei?
    • Memory yakagoverwa kune iyo muenzaniso
    • Nhamba yeSMS
    • Iyo PCIe bandwidth
    • Kumhanya kwewachi
  5. Ndeupi mutoro webasa unonyanya kukodzera MIG?
    • Imwe hombe yekudzidziswa kumhanya inoda iyo GPU yese
    • Multi-rentant inference inoshandira ine inofanorongeka latency
    • CPU-yakasungwa data preprocessing
    • Kuchengetedza kwenguva refu kunotonhora
  6. Ko Kubernetes cluster inogona sei kufumura MIG zviitiko kune pods?
    • Sezvo mbishi dhisiki mavhoriyamu
    • Chete kuburikidza nemanyorero SSH
    • Sezvishandiso zvinorongwa se'nvidia.com/mig-1g.5gb'
    • Via DNS zvinyorwa
  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