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ServerlessT2I is presented, a serverless-native system that decomposes a T2I workflow into loosely coupled model functions that can be independently managed and scheduled and introduces a fair scheduler for multi-tenant serving.
Xema is presented, a memory-efficient diffusion serving system that exploits predictable tensor lifetimes for trace-guided memory optimization and introduces an offline planner that jointly selects parallelism, concurrency, and memory control under GPU memory and SLO constraints.
Rollplex is presented, a runtime that decomposes the reference and training phase and moves the prefix computation into the rollout decode window and achieves speedup over serial colocation and disaggregation under the same GPU budget, while preserving the synchronous RL update.
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