Training Million-Token Context Without Selling Your Kidney
You can fit a million-token context model on consumer GPUs now. Not through clever tricks or compression hacks — through Ring Attention, a distributed attention mechanism that splits sequence length across devices instead of batching. The math looks deceptively simple: instead of computing full $O(N^2)$ attention on one GPU, you partition the sequence into chunks and pass KV states in a ring topology. Each GPU handles $O(N^2/P)$ memory where $P$ is the number of devices.
But here's the part no one tells you upfront: the communication overhead will destroy you if you don't tune it right. I spent two weeks chasing a 4x slowdown that turned out to be synchronous all-reduce calls blocking the ring transfers.
This post walks through Ring Attention from first principles, shows you the exact memory calculations, and demonstrates training on 512K tokens using 4x RTX 3090s (24GB each). Then we'll push it to 1M tokens and watch where it breaks.
Continue reading the full article on TildAlice
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