success is the combination of luck and preparation. NVIDIA has been well prepared + anticipating deep learning to take off for a long while.
not a fan of these kinds of arguments. the 'correct' token is entirely dependent on the dataset. a LLM could have perfect training loss given a dataset, but this has no predictive power on its ability to 'answer'…
> ChatGPT is neat. For all we know we’re near a local maxima of what we’re capable of achieving without another completely new approach that will take 10 or 15 years to figure out. There’s no proof that the acceleration…
competitive with H100 for inference. a 2 year old product on just one half of the ML story. H200 (and potentially B100) is the appropriate comparison based on their production in volume.
> the AMD is typically cheaper and less power hungry than the equivalent Nvidia cheaper is true, but less power hungry is absolutely not true, which is kind of my point.
when was the last time AMD hardware was keeping up with NVIDIA? 2014?
success is the combination of luck and preparation. NVIDIA has been well prepared + anticipating deep learning to take off for a long while.
not a fan of these kinds of arguments. the 'correct' token is entirely dependent on the dataset. a LLM could have perfect training loss given a dataset, but this has no predictive power on its ability to 'answer'…
> ChatGPT is neat. For all we know we’re near a local maxima of what we’re capable of achieving without another completely new approach that will take 10 or 15 years to figure out. There’s no proof that the acceleration…
competitive with H100 for inference. a 2 year old product on just one half of the ML story. H200 (and potentially B100) is the appropriate comparison based on their production in volume.
> the AMD is typically cheaper and less power hungry than the equivalent Nvidia cheaper is true, but less power hungry is absolutely not true, which is kind of my point.
when was the last time AMD hardware was keeping up with NVIDIA? 2014?