diff --git a/comfy/samplers.py b/comfy/samplers.py
index 6fd0387d5..42b05f3ba 100755
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -465,6 +465,9 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t
 
     def _handle_batch(device: torch.device, batch_tuple: tuple[comfy.hooks.HookGroup, tuple], results: list[thread_result]):
         try:
+            # TODO: non-NVIDIA support -- guard with `if device.type == "cuda":` once
+            # we extend multigpu QA beyond CUDA. Unconditional call crashes on
+            # XPU/NPU/MPS/CPU/DirectML backends.
             torch.cuda.set_device(device)
             model_current: BaseModel = model_options["multigpu_clones"][device].model
             # run every hooked_to_run separately
@@ -524,6 +527,12 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t
                         output = model_options['model_function_wrapper'](model_current.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).to(output_device).chunk(batch_chunks)
                     else:
                         output = model_current.apply_model(input_x, timestep_, **c).to(output_device).chunk(batch_chunks)
+                    # TODO: non-NVIDIA support -- the `.to(output_device)` copies
+                    # above are async on CUDA, so the main thread's aggregation
+                    # could race with in-flight transfers. CUDA-only QA has not
+                    # surfaced this in practice, but before extending multigpu
+                    # beyond NVIDIA add a `torch.cuda.synchronize(output_device)`
+                    # here (guarded by `output_device.type == "cuda"`).
                     results.append(thread_result(output, mult, area, batch_chunks, cond_or_uncond))
         except Exception as e:
             results.append(thread_result(None, None, None, None, None, error=e))