2) The details say that the performance numbers require 2.7x the overall power, so around 13.5W. Power needed for all the memory is not explicit in the numbers.
3) The compiler is also simulated and it's a prototype, so it's not a drop-in replacement for anything like Nvidia ISAAC
4) 5W seems to refer to sub-4B models, so not the 100B param ones.
This mentions targets for inference of 10 to 30tps and I (albeit just skimming through) didn't find any info about ingestion speed. They also mention that the 1st gen chip is used for vision, though I suspect it's only for specific cases and not anything general.
Overall I like power efficient optimizations like this and let's hope that this is adopted broadly. Although the compute in memory sounds like a big increase in memory prices
Quirks from the article:
1) The performance numbers are simulated
2) The details say that the performance numbers require 2.7x the overall power, so around 13.5W. Power needed for all the memory is not explicit in the numbers.
3) The compiler is also simulated and it's a prototype, so it's not a drop-in replacement for anything like Nvidia ISAAC
4) 5W seems to refer to sub-4B models, so not the 100B param ones.
The chip is designed to run models with 20 billion to 100 billion parameters (approximately 3 billion to 20 billion active parameters)
Doesn't seem like 100B dense models are even on the table, only MoE models. Kinda misleading.
I think the human brain uses something on the order of 10-20W so 13.5W seems like there's probably some room to improve on a 4b model!
423. Could y'all please start prefixing comments with the number of joules you consumed in generating it, so that I can decide if it's worth reading?
This mentions targets for inference of 10 to 30tps and I (albeit just skimming through) didn't find any info about ingestion speed. They also mention that the 1st gen chip is used for vision, though I suspect it's only for specific cases and not anything general.
Overall I like power efficient optimizations like this and let's hope that this is adopted broadly. Although the compute in memory sounds like a big increase in memory prices
> "large large language models"
Wonder how it would feel because the details shown seems a bit slow to edge device use