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Context and Sequence Parallelism

Context parallelism splits a single long sequence across GPUs so that attention over 128k tokens fits in memory that would otherwise need terabytes of activations per layer. Ring attention rotates key-value blocks around the ranks while queries stay put, and grouped-query attention is what makes that rotation cheap enough to hide behind the attention math.

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RELATED CONCEPTS

Tensor Parallelism Activation Checkpointing Collective Communication Primitives FlashAttention Internals Attention Variants: MHA, GQA, MQA and MLA KV Cache Sizing

LESSONS THAT TEACH THIS

Training at ScaleMemory decides your parallel layout before you get an opinion14 min→

PRACTICE THIS IN REAL QUESTIONS

Distributed Training & ParallelismHow do sequence parallelism and context parallelism make 128k-context training of a 405B possible, and what do they cost?→Distributed Training & ParallelismWhy does tensor parallelism stop at 8? Show me the numbers.→Napkin Math, Cost & CapacityEstimate the activation memory for training a 70B model at an 8k sequence length→Napkin Math, Cost & CapacityHow big is the KV cache for Llama 3.1 70B at a 128k context?→LLM Inference & ServingCompare the KV cache footprint of multi-head, grouped-query and multi-head latent attention with numbers.→Open-Weights Models & Serving EnginesThe model advertises a million tokens of context. What actually breaks when you serve it?→