diff --git a/examples/models/gemma4/text_decoder/gemma4_decoder_layer.py b/examples/models/gemma4/text_decoder/gemma4_decoder_layer.py
index e10c1c7e415..fe3e3bb94cb 100644
--- a/examples/models/gemma4/text_decoder/gemma4_decoder_layer.py
+++ b/examples/models/gemma4/text_decoder/gemma4_decoder_layer.py
@@ -34,14 +34,25 @@ class Gemma4MLP(nn.Module):
 
     def __init__(self, hidden_size: int, intermediate_size: int):
         super().__init__()
+        self.intermediate_size = intermediate_size
         self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
         self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
         self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.down_proj(
-            F.gelu(self.gate_proj(x), approximate="tanh") * self.up_proj(x)
-        )
+        # If a loader fused gate_proj|up_proj into one gate_up_proj (single
+        # matmul; e.g. the GGUF loader's coalesced fusion), use it and split the
+        # [.., 2*intermediate_size] output back into gate/up. Otherwise fall back
+        # to the separate projections (unfused checkpoints / non-fusing loaders).
+        gate_up = getattr(self, "gate_up_proj", None)
+        if gate_up is not None:
+            fused = gate_up(x)
+            gate = fused[..., : self.intermediate_size]
+            up = fused[..., self.intermediate_size :]
+        else:
+            gate = self.gate_proj(x)
+            up = self.up_proj(x)
+        return self.down_proj(F.gelu(gate, approximate="tanh") * up)
 
 
 class Gemma4DecoderLayer(nn.Module):
diff --git a/examples/models/gemma4_31b/gguf_loader.py b/examples/models/gemma4_31b/gguf_loader.py
index 90839ea6f6a..6a4a70ced18 100644
--- a/examples/models/gemma4_31b/gguf_loader.py
+++ b/examples/models/gemma4_31b/gguf_loader.py
@@ -104,6 +104,89 @@ def _convert_weight(model, model_key: str, gtensor, backend: str):
     return gtensor
 
 
+# ---------------------------------------------------------------------------
+# Single-point gate/up fusion (backend-agnostic, at the raw GGUF level)
+#
+# gate_proj and up_proj share the same input, so the MLP can issue ONE matmul
+# over a [2*intermediate, hidden] weight instead of two. We fuse here -- before
+# any backend conversion (_convert_weight) -- by concatenating the two raw GGUF
+# block blobs along the output (row) dim. ExportableGGUFTensor.raw is
+# (N, row_bytes) row-major with each output row self-contained, so the concat is
+# an exact row-stack (no re-quant, no scale recompute). Both CUDA and MLX then
+# pack the already-fused weight, so there is no per-backend-type concat. The
+# model's Gemma4MLP.forward splits the [.., 2*intermediate] output back into
+# gate/up only when a fused gate_up_proj is present (graceful for unfused loads).
+
+
+def _gate_up_layer_kind(model_key: str):
+    """If ``model_key`` is an MLP gate/up proj weight, return ``(layer_idx, kind)``
+    with ``kind`` in ``{"gate", "up"}``; otherwise ``None``."""
+    prefix = "layers."
+    for kind in ("gate", "up"):
+        suffix = f".mlp.{kind}_proj.weight"
+        if model_key.startswith(prefix) and model_key.endswith(suffix):
+            mid = model_key[len(prefix) : len(model_key) - len(suffix)]
+            if mid.isdigit():
+                return int(mid), kind
+    return None
+
+
+def _gate_up_fuseable(gate, up) -> bool:
+    """True iff gate/up are the same GGUF quant type and same packed row width
+    (hence same K + block layout), so a row-concat along output N is valid."""
+    return (
+        gate.ggml_type == up.ggml_type
+        and gate.raw.shape[1] == up.raw.shape[1]
+        and int(gate.shape[1]) == int(up.shape[1])
+    )
+
+
+def _fuse_gate_up_raw(gate, up):
+    """Row-concat gate|up raw GGUF blocks (gate rows first) into one fused
+    ExportableGGUFTensor of shape (2*N, K)."""
+    from executorch.extension.llm.export.gguf import ExportableGGUFTensor
+
+    fused_raw = torch.cat([gate.raw, up.raw], dim=0)
+    return ExportableGGUFTensor.from_raw(fused_raw, gate.ggml_type, gate.orig_dtype)
+
+
+def _assign_gate_up_unfused(model, layer_idx, kind, gtensor, backend, packers):
+    """Assign a single gate/up GGUF tensor to its own projection (no fusion)."""
+    from executorch.examples.models.gemma4_31b.quant import pack_one
+
+    key = f"layers.{layer_idx}.mlp.{kind}_proj.weight"
+    pack_one(model, key, _convert_weight(model, key, gtensor, backend), packers)
+
+
+def _install_and_pack_fused_gate_up(model, layer_idx, gate, up, backend, packers):
+    """Fuse gate|up at the raw level, swap the layer's MLP to a single
+    ``gate_up_proj`` (dropping gate_proj/up_proj), then pack the fused weight."""
+    import torch.nn as nn
+
+    from executorch.examples.models.gemma4_31b.quant import pack_one
+
+    fused = _fuse_gate_up_raw(gate, up)
+    inter, hidden = int(gate.shape[0]), int(gate.shape[1])
+
+    mlp = model.get_submodule(f"layers.{layer_idx}.mlp")
+    mlp.gate_up_proj = nn.Linear(hidden, 2 * inter, bias=False, device="meta")
+    del mlp.gate_proj
+    del mlp.up_proj
+
+    key = f"layers.{layer_idx}.mlp.gate_up_proj.weight"
+    pack_one(model, key, _convert_weight(model, key, fused, backend), packers)
+
+
+def _process_gate_up_pair(model, layer_idx, gate, up, backend, packers) -> bool:
+    """Fuse gate|up if compatible (returns True), else assign them unfused."""
+    if _gate_up_fuseable(gate, up):
+        _install_and_pack_fused_gate_up(model, layer_idx, gate, up, backend, packers)
+        return True
+    _assign_gate_up_unfused(model, layer_idx, "gate", gate, backend, packers)
+    _assign_gate_up_unfused(model, layer_idx, "up", up, backend, packers)
+    return False
+
+
 def _resolve_tied_lm_head(model, lm_head_weight, packers):
     """Assign a tied lm_head (GGUF ties it to the token embedding)."""
     from executorch.examples.models.gemma4_31b.quant import pack_one
@@ -217,11 +300,32 @@ def load_gguf_model(
     n_processed = 0
 
     print(f"Streaming GGUF from {gguf_path}...")
+    pending_gate_up: dict = {}  # layer_idx -> {"gate": raw, "up": raw}
+    n_fused = 0
+    n_unfused = 0
     for gguf_name, value in iter_gguf(gguf_path):
         model_key = gguf_to_model_key(gguf_name)
         if model_key is None:
             continue
 
+        # Buffer the RAW gate/up ExportableGGUFTensor (pre-conversion) and fuse
+        # once both arrive -- the single common point upstream of _convert_weight.
+        gu = _gate_up_layer_kind(model_key)
+        if gu is not None and isinstance(value, ExportableGGUFTensor):
+            layer_idx, kind = gu
+            slot = pending_gate_up.setdefault(layer_idx, {})
+            slot[kind] = value
+            if "gate" in slot and "up" in slot:
+                if _process_gate_up_pair(
+                    model, layer_idx, slot["gate"], slot["up"], backend, packers
+                ):
+                    n_fused += 1
+                else:
+                    n_unfused += 1
+                pending_gate_up.pop(layer_idx, None)
+                n_processed += 2
+            continue
+
         if isinstance(value, ExportableGGUFTensor):
             weight = _convert_weight(model, model_key, value, backend)
             if model_key == "embed_tokens.weight":
@@ -238,6 +342,21 @@ def load_gguf_model(
         if n_processed % 100 == 0:
             print(f"  Processed {n_processed} tensors...")
 
+    # Flush any unpaired gate/up (partial/malformed) as separate unfused
+    # projections so no weight is left on meta.
+    for layer_idx, slot in pending_gate_up.items():
+        for kind in ("gate", "up"):
+            if kind in slot:
+                _assign_gate_up_unfused(
+                    model, layer_idx, kind, slot[kind], backend, packers
+                )
+                n_unfused += 1
+
+    print(
+        f"[gemma4_31b gguf] Fused gate+up on {n_fused} MLP layers"
+        + (f" ({n_unfused} left unfused)" if n_unfused else "")
+    )
+
     _resolve_tied_lm_head(model, lm_head_weight, packers)
 
     # Fill RoPE tables / KV caches / scalar constants (left on meta by the