TensorRT iyo Kediska Matoorada Inference

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. What is the goal of building an inference engine with TensorRT?
   • Guarantee the fastest possible execution on every system
   • Optimize execution of a supported trained model for the configured target
   • Choose training labels without a dataset
   • Replace every GPU with a CPU
2. Sidee isku-dhafka lakabka u dedejiyaa fikradda?
   • Iyada oo la isku darayo hawlo badan oo hal kernel GPU ah si loo yareeyo socodka xusuusta
   • Adigoo tirtiraya lakabyada aan loo baahnayn
   • Adigoo ku socodsiinaya lakab kasta GPU gooni ah
   • Adigoo u beddelaya moodelka inuu ku shaqeeyo CPU
3. In a representative-data INT8 calibration workflow, why examine the model’s value distributions?
   • To guarantee no accuracy change under all future inputs
   • To retrain the model from zero automatically
   • To select quantization scales suited to the observed numerical ranges
   • To remove every compatibility requirement
4. Waa maxay sababta matoorka TensorRT ee la soo ururiyey uu guud ahaan gaar ugu yahay hal dhisme oo GPU ah?
   • Sababtoo ah waxaa lagu sireeyay lambarka taxan ee GPU-ga
   • Sababtoo ah NVIDIA waxay siisay shatiga qalabkiiba
   • Sababtoo ah waxay ku kaydisaa xogta tababarka gudaha mishiinka
   • Sababtoo ah kernel auto-tuning waxay ku dubtaa kernels-ka ugu dhaqsaha badan ee GPU-da saxda ah ee xudunta u ah iyo qaabka xusuusta
5. Waa kuwee sifada TensorRT-LLM oo si gaar ah u caawisa in si hufan loogu adeego moodooyinka luqadaha waaweyn?
   • Duulimaadka Duulimaadka (oo socota) Duubista iyo baakadaha KV-cache
   • Kordhinta sawirka tooska ah
   • Ku-dhismay martigelinta shabakada
   • Joojinta qaylada maqalka
6. Why evaluate task accuracy after choosing lower-precision TensorRT execution?
   • Lower precision always preserves every output bit
   • Only the model filename affects predictions
   • Reduced numerical precision can change model outputs or quality
   • Accuracy evaluation is needed only during tokenization
7. A TensorRT dynamic-shape engine receives input outside its configured profile range. What should deployment validation check?
   • Whether the engine can accept every shape without a profile
   • Only whether the model name is short
   • Whether training can restart from a screenshot
   • Whether the profile supports that requested shape
8. A TensorRT engine is moved to a different runtime and GPU configuration. What must the team establish?
   • That any serialized engine runs unchanged everywhere
   • That the selected compatibility settings support the new environment
   • That the engine contains all its original training data
   • That every GPU shares one serial number