Standalone reference pieces, updated occasionally rather than weekly, for the concepts that keep coming up across issues.
Why training needs roughly 8x the memory footprint of serving the same model, and how that gap is what makes large-scale training a parallelism problem rather than a bigger-GPU problem.
A visual walkthrough of the data flow from a stored checkpoint to a generated token, and why the memory footprint keeps growing after the model has already loaded.