Low-Rank Adaptation (LoRA) has become a popular method to adapt pre-trained models to specific domains. Applied to encoders in a multi-tenant service, it lets each tenant specialize a shared model for classification, retrieval, or ranking. Existing multi-tenant LoRA serving systems target autoregressive decoders, and for encoders the available tooling supports only per-request adapter swapping, whose cost grows with the number of registered adapters. We observe that, unlike decoder generation, the encoder forward pass is compute-bound and dominated by the shared base projections, so per-tenant adaptation can be expressed as plain batched matrix multiplication with no custom kernels. We present LateFuse, a pure-PyTorch system that serves many different LoRA-adapted encoders in a single shared forward pass on one GPU. LateFuse fuses adapters late, in activation space: the base projections run once over the mixed-tenant batch, while per-sample low-rank deltas and per-tenant classification heads are applied as batched matrix multiplications. Tenant weights are never merged into the shared base, ruling out cross-tenant contamination by construction. Our evaluation shows that LateFuse serving latency stays flat as the adapter pool grows to tens of thousands on a single GPU, and that it achieves 5.6–21.2x higher throughput than HuggingFace PEFT’s native mixed-batch API and up to 521× over per-adapter swapping, while registering new adapters over 299x faster.