diff --git a/src/llmcompressor/pipelines/sequential/cache.py b/src/llmcompressor/pipelines/sequential/cache.py
new file mode 100644
index 00000000..7d2defb7
--- /dev/null
+++ b/src/llmcompressor/pipelines/sequential/cache.py
@@ -0,0 +1,94 @@
+import warnings
+from dataclasses import dataclass
+from typing import Any, Dict, List, Union
+
+import torch
+import tqdm
+
+from llmcompressor.modifiers.utils.pytorch_helpers import apply_pad_mask_to_batch
+
+
+@dataclass
+class IntermediateValue:
+    value: Any
+    device: Union[torch.device, None]
+
+
+class IntermediatesCache:
+    batch_intermediates: List[Dict[str, IntermediateValue]]
+    offload_device: torch.device
+
+    def __init__(
+        self,
+        batch_intermediates: List[Dict[str, IntermediateValue]],
+        offload_device: torch.device,
+    ):
+        self.batch_intermediates = batch_intermediates
+        self.offload_device = offload_device
+
+    @classmethod
+    def from_dataloader(
+        cls,
+        dataloader: torch.utils.data.DataLoader,
+        model_device: torch.device,
+        mask_padding: bool = True,
+        offload_device: torch.device = "cpu",
+    ):
+        batch_intermediates = []
+        for batch in tqdm.tqdm(dataloader, desc="Preparing intermediates cache"):
+            if mask_padding and "attention_mask" in batch:
+                batch = apply_pad_mask_to_batch(batch)
+            batch = {
+                key: IntermediateValue(value=value, device=model_device)
+                for key, value in batch.items()
+            }
+            batch_intermediates.append(batch)
+
+        return cls(batch_intermediates, offload_device)
+
+    def fetch(self, batch_index: int, input_names: List[str]) -> Dict[str, Any]:
+        intermediates = self.batch_intermediates[batch_index]
+
+        return {
+            key: self._onload_value(subgraph_input)
+            for key, subgraph_input in intermediates.items()
+            if key in input_names
+        }
+
+    def update(self, batch_index: int, outputs: Dict[str, Any]):
+        # assume that all model intermediates are tensors
+        assert (isinstance(value, torch.Tensor) for value in outputs.values())
+
+        intermediates = {
+            key: self._offload_value(value) for key, value in outputs.items()
+        }
+
+        self.batch_intermediates[batch_index].update(intermediates)
+
+    def delete(self, batch_index: int, consumed_names: List[str]):
+        intermediates = self.batch_intermediates[batch_index]
+        for name in consumed_names:
+            del intermediates[name]
+
+    def _onload_value(self, intermediate: IntermediateValue) -> Any:
+        value = intermediate.value
+        device = intermediate.device
+
+        if device is not None:
+            if isinstance(value, torch.Tensor):
+                return value.to(device=device)
+            else:
+                raise NotImplementedError("Intermediates")
+
+        else:
+            return value
+
+    def _offload_value(self, value: Any) -> IntermediateValue:
+        if isinstance(value, torch.Tensor):
+            return IntermediateValue(
+                value=value.to(device=self.offload_device), device=value.device
+            )
+
+        else:
+            warnings.warn(f"Offloading not implemented for type {type(value)}.")
+            return IntermediateValue(value=value, device=None)