Sequence Parallelism in Large Language Models: How Megatron-SP, DeepSpeed Ulysses, and RingAttention Distribute Long Contexts
Sequence Parallelism in Large Language Models: How Megatron-SP, DeepSpeed Ulysses, and RingAttention Distribute Long Contexts Training and serving frontier large language models on context windows spanning hundreds of thousands to millions of tokens introduces a fundamental memory barrier. While model parameters can be distributed across GPUs using Tensor Parallelism (TP) or Fully Sharded Data Parallelism (FSDP / ZeRO), activation memory scales directly with sequence length $S$. For sequence le
1 min