Ring Attention
A distributed attention technique that distributes long sequences across multiple GPUs by passing KV blocks in a ring between devices.
Ring Attention distributes attention in a ring across GPUs – enables million-token contexts by overlapping compute and communication.
Explanation
Each GPU holds a portion of the sequence and computes local attention. KV blocks are sent ring-wise to the next GPU while attention is simultaneously computed. This overlaps communication and compute, enabling extremely long contexts (1M+ tokens).
Marketing Relevance
Ring Attention enables million-token contexts like Gemini (2M) – without overloading a single GPU's memory.
Common Pitfalls
Requires fast inter-GPU communication (NVLink). Latency with small batch sizes. Not trivial to implement.
Origin & History
Liu et al. (UC Berkeley, 2023) introduced Ring Attention. Gemini 1.5 (Google, 2024) used similar techniques for 2M token context. The method combines ideas from Flash Attention with sequence parallelism.
Comparisons & Differences
Ring Attention vs. Flash Attention
Flash Attention optimizes attention on one GPU (IO efficiency); Ring Attention distributes attention across GPUs (memory scaling).
Ring Attention vs. Tensor Parallelism
Tensor parallelism splits model weights across GPUs; Ring Attention splits the sequence across GPUs.