DBO++: Rethinking Communication Overlap for Dense Tensor-Parallel Prefill

Communication overlap can reduce prefill latency in dense language models, but its value depends on more than the input batch. We present DBO++, which extends vLLM's dual-batch execution to dense tensor-parallel prefill, and study how engine configuration changes the choice between overlapping and unsplit execution. On Qwen3-32B with four H200 GPUs, changing the configured batch-token limit while keeping the input and split fixed changes the mean effect from a 10.1% slowdown to a 3.6% latency reduction. Controlled comparisons connect this reversal to startup profiling and memory allocation. In the measured setting, live allocation increases allocator retries and imposes a penalty concentrated in the overlapping path. A targeted intervention preserves existing KV capacity while changing subsequent allocation handling; measured retries disappear and a 24.6% live-allocation penalty falls to approximately zero. Separately, a matched comparison with fixed KV allocation establishes a 2.1% latency reduction over unsplit execution. A rule using workload information and the configured limit preserves a token-only rule's positive savings while avoiding its two harmful choices in an initial held-out evaluation. Independent follow-up tests expose output mismatches and slower selections, delimiting this empirical rule's support. A broader four-model, one-output-token survey records both gains and regressions. Together, these results establish configuration-dependent overlap in the studied system and show why execution selection must be evaluated with the engine conditions and output contract under which it is used.