mixed precision
Applied AI interview questions tagged mixed precision, across every topic.
5 questions · 1 unlocked for you
Concepts behind "mixed precision"
The curriculum that explains the ideas these questions test.
Core
Quantization and Low PrecisionQuantization stores and computes model weights (and activations) in fewer bits, FP16/BF16, FP8, INT8, INT4, instead of FP32, cutting memory and speeding inference at some accuracy cost. It is the main lever to fit a large model on a given GPU and to serve it cheaply, and it underlies QLoRA fine-tuning and KV-cache compression. Applied-AI interviews probe it because 'how do you serve a 70B model affordably?' usually starts with quantization, and knowing the precision ladder and its trade-offs is essential.🖥️ ML Infrastructure & ServingSign in
Core
Mixed-Precision TrainingMixed-precision training does most computation in 16-bit (FP16 or BF16) instead of 32-bit, roughly halving memory and speeding up training on modern GPUs, while keeping a few numerically-sensitive parts in FP32 for stability. BF16 is preferred over FP16 because it keeps FP32's exponent range, avoiding the overflow/underflow that FP16 needs loss scaling to handle. Applied-AI interviews probe it because it is standard practice for training at scale and a clean example of the precision-vs-stability trade-off.🖥️ ML Infrastructure & ServingSign in