diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 8a32bf510849e0..ea9daca826366b 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -4119,8 +4119,26 @@ class GraniteMoeModel(GraniteModel):
     """Conversion for IBM's GraniteMoeForCausalLM"""
     model_arch = gguf.MODEL_ARCH.GRANITE_MOE
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        """In modeling_granitemoe, the JetMoe implementation of parallel experts
+        is used. This essentially merges w1 and w3 into a single tensor with 2x
+        the hidden size that is then split during forward. To keep compativility
+        with existing mixtral support, we pull them apart here.
+        """
+
+        if name.endswith("block_sparse_moe.input_linear.weight"):
+            gate, up = data_torch.chunk(2, dim=-2)
+            return [
+                (self.map_tensor_name(f"model.layers.{bid}.block_sparse_moe.input_linear.gate.weight"), gate),
+                (self.map_tensor_name(f"model.layers.{bid}.block_sparse_moe.input_linear.up.weight"), up),
+            ]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
 ###### CONVERSION LOGIC ######
 
+
 # tree of lazy tensors
 class LazyTorchTensor(gguf.LazyBase):
     _tensor_type = torch.Tensor
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 4e109122080d89..fed7418bee37c2 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -1254,6 +1254,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ATTN_OUT,
         MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index fc5fb30109b4ca..901c03c7ce11c4 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -293,11 +293,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
-            "layers.{bid}.feed_forward.experts.w3",             # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear_v",     # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.v1",      # dbrx
-            "model.layers.{bid}.mlp.experts.up_proj",           # qwen2moe olmoe (merged)
-            "model.layers.{bid}.block_sparse_moe.input_linear", # granitemoe
+            "layers.{bid}.feed_forward.experts.w3",                # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_v",        # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.v1",         # dbrx
+            "model.layers.{bid}.mlp.experts.up_proj",              # qwen2moe olmoe (merged)
+            "model.layers.{bid}.block_sparse_moe.input_linear.up", # granitemoe
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
@@ -326,10 +326,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
-            "layers.{bid}.feed_forward.experts.w1",             # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear",       # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.w1",      # dbrx
-            "model.layers.{bid}.mlp.experts.gate_proj",         # qwen2moe olmoe (merged)
+            "layers.{bid}.feed_forward.experts.w1",                  # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear",            # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w1",           # dbrx
+            "model.layers.{bid}.mlp.experts.gate_proj",              # qwen2moe olmoe (merged)
+            "model.layers.{bid}.block_sparse_moe.input_linear.gate", # granitemoe
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (