Diffusion Transformer (DiT) is the standard architecture for FLUX, Wan, CogVideoX and other image/video generation models of the moment, hitting SOTA for real, but inference is also expensive. Multi-step sampling stacked on top of parameter scale, DiT's deployment cost is always pressing at the door of "real-time interaction" and "on-device". Post-training quantization (PTQ) is generally seen as the optimal solution — but DiT's activation distribution drifts between timesteps, prompts, and guidance branches, so old methods need to re-run a round of calibration data for every checkpoint swap and every mode crossover, which is practically unusable. OrbitQuant (arXiv:2607.02461) sidesteps range estimation directly: it quantizes on a normalized, rotated basis. The core is a layer of randomized permuted block-Hadamard (RPBH) rotation, which can "press" any input's coordinates close to the same fixed, known marginal distribution — that is, a single Lloyd-Max codebook can cover all timesteps, prompts, and layers under the same input dimension; weight rows absorb the rotation offline the same way, with only one forward rotation on the activation side at runtime; the same set of recipes transfers from image to video without re-tuning, "one quantization, cross-modality deployment" thus has an engineering basis. Tested across FLUX.1, Z-Image-Turbo, Wan 2.1, CogVideoX, it refreshes PTQ SOTA across multiple low-bit tiers, with the most aggressive pushing image DiT's PTQ to W2A4 and preserving usable generation quality. For deployment, OrbitQuant kicks away the unwritten rule of "quantized model = recalibration": training-side quantization is no longer locked to the checkpoint, and runtime inference-side also escapes the pain of layer-by-layer tuning. For DiT to truly run on-device, this is one of the few "one-time quantization, universal deployment" directions.