That's all fine and good. But to do anything useful, you're going to want a powerful GPU (RTX 3090, RTX 4090 or A6000) with as much VRAM as possible. Unlike the diffusion models, LLM's are very memory-intensive, even at 4-bit GPTQ. The larger models like llama-13b and llama-30b run quite well at 4-bit on a 24GB GPU. The llama-65b-4bit should run on a dual 3090/4090 rig. Coupled with the leaked Bing prompt and text-ge…
Here is some quick math: these devices has SSD read speed somewhere around 2GiB/s. With 4-bit quantization, we are looking at loading 4B parameters per second. That means we need 8s per token for 30B model. Hmmm, the math is a bit off (or I need to look closer whether we can do more tokens per iteration with some batching).
Using LRDIMM DDR4 at the price of less than $1000 it is possible to stream GPT-3 five times a second, in 4bit quantisation. Multiply that with the 2-2.5x speedup from Speculative Sampling.
>Accelerating Large Language Model Decoding with Speculative Sampling