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Traditional training stores weights and runs math in 32-bit floating point (FP32). Mixed precision uses lower-precision FP16 or bfloat16 for eligible operations while retaining FP32 where needed. Some FP16 recipes maintain a separate FP32 master copy to accumulate small weight updates; autocast workflows may instead keep model parameters in FP32 while casting selected operation inputs. Using 16-bit tensors can reduce memory used by eligible operations. Supported accelerators may execute some low-precision operations faster, but end-to-end throughput depends on hardware, workload and which operations autocast selects. FP16 has a narrower range, so tiny gradients can underflow. Loss scaling multiplies the loss before backpropagation and reverses that scale before the optimizer update; tools such as torch.amp.GradScaler can manage this process. Bfloat16 has a wider exponent range than FP16 and often does not need the same loss-scaling strategy, though model and hardware behavior still need testing.