diff --git a/src/accelerate/utils/fsdp_utils.py b/src/accelerate/utils/fsdp_utils.py
index 6897888c0e4..233aabf0fc7 100644
--- a/src/accelerate/utils/fsdp_utils.py
+++ b/src/accelerate/utils/fsdp_utils.py
@@ -658,13 +658,28 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
         )
 
     model_has_params4bit = False
+    incompatible_params4bit = set()
     for name, param in model.named_parameters():
         # this is a temporary fix whereby loading models with bnb params cannot be moved from
         # GPU to a meta device due with FSDP2 because torch operations don't return the original class type
         # bypassing the move to meta will still cause the VRAM spike, but at least it still will load
         if param.__class__.__name__ == "Params4bit":
             model_has_params4bit = True
-            break
+            # Exclude non-floating frozen Params4bit from FSDP sharding.
+            # Default uint8 quant_storage cannot survive fully_shard's DTensor conversion.
+            if (not param.requires_grad) and (not param.is_floating_point()) and (not param.is_complex()):
+                incompatible_params4bit.add(param)
+
+    if incompatible_params4bit and is_torch_version(">=", "2.7.0"):
+        ignored = set(fsdp2_kwargs.get("ignored_params", set()))
+        fsdp2_kwargs["ignored_params"] = ignored | incompatible_params4bit
+        if accelerator.is_main_process:
+            warnings.warn(
+                f"Found {len(incompatible_params4bit)} non-floating frozen Params4bit. "
+                "Excluding from FSDP2 sharding to prevent quant_state corruption."
+                "To enable memory-efficient sharding of 4-bit weights, set"
+                "bnb_4bit_quant_storage to a floating dtype (e.g. bf16)."
+            )
 
     if fsdp2_plugin.cpu_ram_efficient_loading and not model_has_params4bit:
         # Context: `fully_shard` moves the model to GPU if it was on CPU, however it can also be on `meta` and then it stays there even after `fully_shard`