You don’t have to spend thousands to experience these superior general intelligence Models. There’s actually a few solutions to fitting those bigger models into your own GPU. In this video I’m covering 3 methods. First is quantization which reduces memory by reducing precision. Weights are stored as numbers and depending on the accuracy the length of the decimal places increases or decreases. Float 32 is really precise because it can capture a floating point number most accurately- 32 1s and 0s vs int 4 in 4 1s and 0s. You loose the tiny decimals but reduce memory. It’s diminishing returns - if you have 3.14 or 3.14159 - the difference is so subtle it doesn’t have a huge impact on quality. Layer steaming and CPU ram offloading are also smart engineering tricks to make it work. In the end it’s a trade off but in most cases it makes sense - however you gain latency. #artificialintelligence #nvidia #localai #engineering #aimodels - @engeldata