How Context Parallelism Splits Long Sequences Across GPUs

Описание: How do you process a long LLM context when its attention workload no longer fits on one GPU? Ring Attention distributes the sequence across devices and circulates key/value blocks—but the network still sets the limits.
In this Skildcore systems explainer, we follow the architecture from KV-cache growth and the failure of a naive split to online softmax, a block-staggered ring, and overlapping communication with computation.
We then examine double buffering, the bandwidth budget, causal masking, pooled memory capacity, and how context parallelism fits into a production parallelism stack. The final section looks at where the approach breaks and why longer context depends on both memory capacity and interconnect performance.
CHAPTERS
00:00 Introduction
00:30 The context wall: KV-cache growth and naive splitting
01:48 Online softmax and the block-staggered ring
03:12 The wire budget: compute, communication, and double buffering
04:36 Kernels, overlap, and causal masks
05:40 Scaling the ring and pooling memory capacity
06:40 Production stacks and sequence partitioning
08:15 Where Ring Attention breaks
08:45 Final takeaway: sequence length, pooled
Which constraint would you hit first in a long-context workload: GPU memory, interconnect bandwidth, or compute? Share your reasoning in the comments.
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