SGLang adds per-PP-rank parallel compilation during startup warmup so all pipeline stages trigger DeepGEMM JIT compiles concurrently instead of sequentially, reducing large MoE model startup from roughly 9 minutes to under 4 minutes on multi-GPU setups.
Parallelizes DeepGEMM JIT kernel compilation across pipeline-parallel ranks during model startup warmup, eliminating serial stage-by-stage compilation bottlenecks for large mixture-of-experts models.
Operators running large MoE models like DeepSeek-V4 across multiple GPUs can cut startup time by more than half, meaning fewer idle GPUs during scaling events and faster recovery after redeployments. The optimization is gated behind an environment variable (SGLANG_PP_PARALLEL_DEEPGEMM_WARMUP) and falls back gracefully on failure, but the concrete speedup depends on PP size and cache state, so teams should benchmark their specific cluster topology before assuming uniform gains.