Diffusion language models (dLLMs) generate text by iteratively denoising multiple token positions in parallel, offering an attractive alternative to strictly autoregressive decoding. In practice, however, block-wise dLLM inference exposes a difficult granularity trade-off: small blocks preserve local conditioning but require many denoising steps, whereas large blocks expose more parallelism but can make premature commitments. Existing block-wise acceleration methods either use a fixed block size or adapt block boundaries along a single decoding trajectory, leaving the complementarity among concurrently executed block-size trajectories unused. We show that block-size diversity provides a useful branching dimension. Different block sizes induce related but non-identical KV-cache trajectories: branches often share an initial prefix, bifurcate at semantically decisive positions, and later agree on syntactically lightweight tokens. Motivated by this structure, we propose BlockBatch, a training-free online inference framework that executes multiple block-size branches for the same request inside a batched forward pass. BlockBatch coordinates these branches through confidence-gated token merging, leader-based synchronization, and periodic full-sequence refreshes that re-anchor local block updates to a globally consistent KV state. Across three representative dLLMs and four datasets, BlockBatch reduces denoising NFEs by 26.6% on average and achieves a 1.33× average end-to-end speedup, with comparable average accuracy.