SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving
No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tensor parallelism, many independent requests favor data-parallel attention, and long prompts favor context parallelism. Reasoning, agentic, and RL-rollout workloads make a fixed choice untenable: a batch that begins as many short requests ends as a few very long ones, so the best layout changes while the same requests run. Serving engines nevertheless fix one layout at launch, because changing it has meant draining requests and restarting workers. We present SPLASH, a serving system that switches the parallel layout of attention while requests are running. It builds on one o...
| Authors | Chuan Liu, Shuoming Zhang, Zhicheng Li et al. |
|---|---|
| Primary category | cs.DC |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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