Maswali ya TensorRT na Injini za Maelekezo

Majibu na maelezo huonekana unapojibu maswali.

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. Muunganisho wa safu huharakisha vipi uelekezaji?
   • Kwa kuunganisha shughuli nyingi kwenye kerneli moja ya GPU ili kupunguza trafiki ya kumbukumbu
   • Kwa kufuta tabaka ambazo hazihitajiki
   • Kwa kuendesha kila safu kwenye GPU tofauti
   • Kwa kubadilisha mfano ili kukimbia kwenye 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. Kwa nini injini ya TensorRT iliyojumuishwa kwa ujumla ni maalum kwa usanifu mmoja wa GPU?
   • Kwa sababu imesimbwa kwa nambari hiyo ya serial ya GPU
   • Kwa sababu NVIDIA inatoa leseni kwa kila kifaa
   • Kwa sababu huhifadhi data ya mafunzo ndani ya injini
   • Kwa sababu urekebishaji kiotomatiki wa kernel huoka katika kokwa za haraka zaidi kwa msingi halisi wa GPU hiyo na mpangilio wa kumbukumbu.
5. Ni kipengele gani cha TensorRT-LLM kinachosaidia hasa kuhudumia miundo mikubwa ya lugha kwa ufanisi?
   • Ukusanyaji wa ndani ya ndege (unaoendelea) na kache ya KV iliyopangwa
   • Uboreshaji wa picha otomatiki
   • Upangishaji wa wavuti uliojengwa ndani
   • Kughairi kelele za sauti
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