AI Precision 64 Vs 8 bit

•	What it is: Uses 64 bits to represent numbers, allowing for very high precision in calculations.
•	Implications:
•	Accuracy: Provides very high numerical accuracy, essential for scientific computations, complex simulations, and training large neural networks.
•	Resource Intensive: Requires more memory and computational power, leading to higher costs and longer processing times.

•	What it is: Uses 8 bits to represent numbers, significantly reducing the range and precision.
•	Implications:
•	Efficiency: Reduces memory usage and computational power, enabling faster processing and lower costs.
•	Performance: Suitable for inference tasks where extreme precision is not critical, such as image recognition and natural language processing.
•	Trade-offs: May introduce minor inaccuracies, but recent advancements have minimized the impact on model performance for many applications.