Part 6/11:
Running such large models requires immense hardware resources. The full 405 billion parameter model demands around 8,810 GB of memory in 16-bit precision, exceeding the capacity of even a single Nvidia DGX H100 system. To address this, Meta has introduced an 8-bit quantized version of Llama 3.1, which cuts down the memory requirement roughly in half without notably sacrificing performance. Quantization techniques like this are pivotal for making large models more deployable in diverse environments.