Query-based segmentation models achieve strong performance even under real-time inference on workstation GPUs.
On edge devices, however, we show their transformer decoders are deeply memory-bandwidth-bound, a bottleneck invisible to standard FLOP analysis that worsens with input resolution and limited DRAM bandwidth.
An FP16 roofline analysis with tensor-core GEMMs active and hardware DRAM counters shows that the non-tensor-core aggregates of FastInst and Mask2Former reach only $2.5\%$ to $6\%$ of the CUDA-core ridge yet account for $53\%$ to $70\%$ of decoder time and $61\%$ to $71\%$ of decoder bytes.
The largest individual kernels sustain up to $90\%$ of nominal peak DRAM bandwidth, effectively saturating the memory bus.
The bottleneck is therefore not primarily low GEMM intensity, but cross-kernel activation traffic.
CompactInst addresses this bottleneck using parameter-free spatial pooling at $4{\times}$, which reduces the bytes moved within the transformer decoder, while a two-convolution refinement head recovers spatial detail.
Under TensorRT FP16 on Jetson Orin Nano, CompactInst delivers decoder speedups from $2.35{\times}$ to $4.74{\times}$ and model speedups from $1.57{\times}$ to $2.62{\times}$ as input resolution grows, while closely matching accuracy ($-0.4$ AP on COCO val2017, $+0.2$ AP on Cityscapes test).
A heavier variant with $2.57{\times}$ more decoder FLOPs still runs its decoder $1.74{\times}$ faster on the most bandwidth-constrained device, confirming at the deployment level that decoder bytes, not FLOPs, govern latency.