These results are generally unimpressive, of course. Most of the improvements at that point are attributable to the authors making use of a stripped down library for autoregressive sampling. HN falling for garbage once again...
These results are generally unimpressive, of course. Most of the improvements at that point are attributable to the authors making use of a stripped down library for autoregressive sampling. HN falling for garbage once again...
It's also a neat result that fp4 quantization doesn't cause much issue even at 175b, though that kinda was to be expected.
The fact that the FlexGen's single-batch generation performance is much worse is unclear to most people not familiar with peculiarities of LLM inference and worth clarifying. Instead, the readme starts with mentioning ChatGPT and Codex - projects that both rely on single-batch inference of LLMs at interactive speeds, which is not really possible with FlexGen's offloading (given the speed mentioned in the parent comment). The batch sizes are not reported in the table as well.
Seeing that, I'm not surprised that most HN commenters misunderstood the project's contribution.