Maareynta Xusuusta GPU iyo Kediska Kala Qaybinta

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay kala qaybsanaanta xusuusta GPU?
   • Marka GPU-gu jir ahaan kala go'o
   • Marka qaabku leeyahay lakabyo badan
   • Marka laba GPU-gu isku khilaafaan natiijada
   • Marka xusuusta xorta ah loo kala qaybiyo qaybo kala firidhsan oo aad u yaryar si loo qanciyo codsi weyn oo xidhiidhsan
2. Waa maxay sababta qaab-dhismeedka sida PyTorch ay u isticmaalaan kaydiyaha xusuusta kaydinta?
   • Si loo sameeyo qaabka mid sax ah
   • Si aad uga fogaato qoondaynta darawalka tooska ah / wicitaanada bilaashka ah adoo dib u isticmaalaya blocks xusuusta la isku daray
   • Si loo sireeyo miisaanka
   • Si loo dhimo tirada GPU-yada loo baahan yahay hal
3. A CUDA allocation fails despite apparently unused reserved memory. What should a developer investigate?
   • Whether fragmentation is proven by the error alone
   • Whether all live tensors have already been freed
   • Whether inactive split blocks or other allocations explain the failure
   • Whether changing the learning rate releases GPU memory
4. Which PyTorch allocator option can be tested for changing-size workloads that leave unused memory slivers?
   • Expandable segments in the allocator configuration
   • Increasing every tensor dimension
   • Disabling the GPU cooling system
   • Increasing the optimizer learning rate
5. Muxuu vLLM's Paged Feejignaanta uu maamulaa si uu u yareeyo qashinka xusuusta inta lagu jiro fikradda?
   • Xogta tababarka modelka
   • Kaydinta qiimaha muhiimka ah (KV), iyadoo la adeegsanayo bogag cabbir go'an
   • Xawaaraha saacada GPU-ga
   • Cabbirka font ee lo'da
6. Farsamokee ayaa yaraynaysa xusuusta firfircoonida si ay uga hortagto khaladaadka xusuusta ka baxsan?
   • Ku darida lakabyo badan
   • Isticmaalka kululaynta heerka waxbarashada dheer
   • Isbaarada hoose (dib u xisaabinta)
   • Ku kaydinta isbaarooyin badan saxanka
7. A live CUDA tensor still has Python references. Will empty_cache release that tensor’s allocation?
   • No; it releases unused cached memory rather than live tensor storage
   • Yes; it deletes every referenced tensor
   • Yes; it moves the tensor automatically to NVMe
   • Only if the tensor contains zero values
8. PyTorch reports reserved memory greater than allocated memory. What distinction explains the gap?
   • Reserved memory measures only CPU RAM
   • Allocated memory includes every process on the GPU
   • The difference directly proves a model accuracy error
   • The allocator can retain cached blocks beyond memory currently used by tensors