diff --git a/thunder/executors/nvfuserex_impl.py b/thunder/executors/nvfuserex_impl.py
index 6c0c34c27b..1788bd5430 100644
--- a/thunder/executors/nvfuserex_impl.py
+++ b/thunder/executors/nvfuserex_impl.py
@@ -786,6 +786,22 @@ def _can_fuse_node(n: Node):
         #   (Used to name fusions like nvFusion0, nvFusion1, ...)
         fusion_counter: int = 0
         for bsyms in bound_symbol_groups:
+            # Related to in-place ops.
+            # prims.copy_ is a no-op for NVFuser in a sense that it does not
+            # generate nor runs any kernels.
+            # For that reason we should avoid fusing prims.copy_ unless
+            # it comes after other non-copy symbols in a fusion.
+            # See the following relevant issues:
+            # https://github.com/Lightning-AI/lightning-thunder/issues/789
+            # https://github.com/Lightning-AI/lightning-thunder/issues/791
+            # NOTE: filter all first "dangling" no-op copies
+            while len(bsyms) > 0 and bsyms[0].sym.id is prims.PrimIDs.COPY_:
+                fused_bsyms.append(bsyms[0])
+                bsyms = bsyms[1:]
+
+            if len(bsyms) == 0:
+                continue
+
             # TODO The following allows generating single node fusions, which
             #   may be suboptimal for real-world performance.
             #   Provide a mechanism to switch between "test" and "perf" modes
diff --git a/thunder/tests/litgpt_model.py b/thunder/tests/litgpt_model.py
index 65aed8c386..0926715952 100644
--- a/thunder/tests/litgpt_model.py
+++ b/thunder/tests/litgpt_model.py
@@ -118,47 +118,8 @@
 name_to_config = {config["name"]: config for config in configs}
 
 
-class OverridenKVCache(nn.Module):
-    def __init__(
-        self,
-        k_shape: tuple[int, int, int, int],
-        v_shape: tuple[int, int, int, int],
-        device: torch.device | None = None,
-        dtype: torch.dtype | None = None,
-    ) -> None:
-        super().__init__()
-        self.register_buffer("k", torch.zeros(k_shape, device=device, dtype=dtype), persistent=False)
-        self.register_buffer("v", torch.zeros(v_shape, device=device, dtype=dtype), persistent=False)
-
-    def forward(self, input_pos: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        # move the buffer to the activation dtype for when AMP is used
-        self.k = self.k.to(k.dtype)
-        self.v = self.v.to(v.dtype)
-        # update the cache
-        # NOTE: `torch._dynamo.is_compiling` is being deprecated, we should update this once all versions have `torch.compiler.is_compiling`.
-        is_compiling = (
-            torch.compiler.is_compiling if hasattr(torch.compiler, "is_compiling") else torch._dynamo.is_compiling
-        )
-        if is_compiling():
-            # inductor doesn't support `index_add` with bfloat16
-            k = self.k.index_copy_(2, input_pos, k)
-            v = self.v.index_copy_(2, input_pos, v)
-            return k, v
-        # See issue: "Support more indexing operators (index_copy and index_add)"
-        k = self.k = torch.index_add(self.k, 2, input_pos, k)
-        v = self.v = torch.index_add(self.v, 2, input_pos, v)
-        # THUNDER bug: cannot return self.k, self.v here (may be cuda graphs related - no minimum repro)
-        return k, v
-
-    def reset_parameters(self) -> None:
-        torch.nn.init.zeros_(self.k)
-        torch.nn.init.zeros_(self.v)
-
-
 import litgpt
 
-# override for operator workarounds
-litgpt.model.KVCache = OverridenKVCache
 # add the testing configurations
 litgpt.config.name_to_config.update(name_to_config)
 name_to_config.update(litgpt.config.name_to_config)