Decode Token Parallelism: Memory-Efficient Distributed Attention Parallelism for Scalable LLM Decoding

Susav Shrestha, Brad Settlemyer, Narasimha Reddy

Serving large language models at high concurrency with long contexts requires KV-cache capacity and attention bandwidth that grow with the decode pool. Existing parallelization strategies trade one against the other: data parallelism replicates model weights, limiting KV capacity; tensor parallelism stops scaling attention at the model's KV-head count and pushes dense collectives across nodes; and decode context parallelism (DCP) recovers capacity by sharding the sequence but adds cross-node aggregation of partial attention results. We observe that during decode, dense layers and attention scale differently: dense layers amortize weight reads across the batch and run efficiently within a single NVLink domain, whereas attention cost grows with every active request's KV cache. We present Decode Token Parallelism (TKNP), a decode-pool architecture that decouples weight placement from attention ownership. TKNP distributes a single model copy across nodes in balanced layer blocks. The active node holding a block executes its projections and MLP for the full batch using intra-node tensor parallelism, while every node computes attention for its own requests against locally resident KV…