diff --git a/python/freetoken/engine/engine.py b/python/freetoken/engine/engine.py
index 018e9622..c386e28b 100644
--- a/python/freetoken/engine/engine.py
+++ b/python/freetoken/engine/engine.py
@@ -1144,6 +1144,21 @@ def _cpu_moe_executor_viable(model_config) -> bool:
         return False
     expert_quant = getattr(model_config, "expert_quant", "none")
     fmt = expert_quant if expert_quant != "none" else (moe_wfmt or "bf16")
+    if fmt == "gguf":
+        # "gguf" is a container tag, not a layout: the checkpoint picks a ggml type per
+        # tensor and the concrete CPU format has to be recovered from the bank types.
+        # Testing the tag against _WFMT_IDS answers False for EVERY GGUF checkpoint, which
+        # silently disables the automatic residency split on hosts where CUDA pinning is
+        # quota-capped (WSL caps it near half of RAM). The symptom is not a clear refusal
+        # but cudaHostRegister failing partway through the banks.
+        from freetoken.moe.cpu_executor import _GGML_TO_CPU_FMT
+
+        types = getattr(model_config, "gguf_expert_types", None)
+        if not types:
+            return False
+        gate_up, down = int(types[0]), int(types[1])
+        # one weight_format serves both banks, so mixed types cannot run on the CPU path
+        return gate_up == down and gate_up in _GGML_TO_CPU_FMT
     return fmt == "mxfp4" or fmt in _WFMT_IDS
 
 
diff --git a/python/freetoken/kernel/csrc/cpu_moe/cpu_moe_ext.cpp b/python/freetoken/kernel/csrc/cpu_moe/cpu_moe_ext.cpp
index 68e6324c..48210b9d 100644
--- a/python/freetoken/kernel/csrc/cpu_moe/cpu_moe_ext.cpp
+++ b/python/freetoken/kernel/csrc/cpu_moe/cpu_moe_ext.cpp
@@ -1650,6 +1650,13 @@ struct CpuMoeExecutor {
       const uint8_t* w = gu_packed_l + ((size_t)e * (2 * I) + row) * (size_t)q4_gu_row_bytes;
       return q6_k_dot_f32_scalar(w, x, H);  // W4A16: bf16 activations, K-quant dequant
     }
+    // Anything that reaches here is assumed NVFP4 and dereferences the scale/global
+    // pointers, which are null for formats that do not have them (the GGUF banks pass 0).
+    // Falling through with an unhandled format therefore segfaults inside the worker
+    // thread rather than reporting anything useful, so reject it here instead.
+    TORCH_CHECK(fmt == WF_NVFP4 || fmt == WF_DSFP4,
+                "cpu_moe gemm1_dot: unhandled weight_format ", fmt,
+                " (handled: bf16=0, nvfp4=1, mxfp4=2, dsfp4=3, q4_0=4, q4_k=5, q6_k=6)");
     const size_t r = (size_t)e * (2 * I) + row;
     if (use_vnni)
       return nvi8dot(gu_packed_l + r * (size_t)(H / 2), gu_scale_l + r * (size_t)(H / 16),
diff --git a/python/freetoken/layers/gguf.py b/python/freetoken/layers/gguf.py
index bc4754e8..5936d34d 100644
--- a/python/freetoken/layers/gguf.py
+++ b/python/freetoken/layers/gguf.py
@@ -280,7 +280,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         flat = x.flatten()
         rows = self.qweight.index_select(0, flat)  # [n, row_bytes] packed
-        y = ggml_dequantize(rows, self._quant_type, flat.shape[0], self.embedding_dim, torch.bfloat16)
+        if self._quant_type in GGML_UNQUANTIZED:
+            # Raw value bytes, not blocks: there is no dequant kernel for the unquantized
+            # types (ggml_dequantize rejects type 1), so reinterpret the gathered rows.
+            y = rows.view(_UNQUANTIZED_DTYPE[self._quant_type]).to(torch.bfloat16)
+        else:
+            y = ggml_dequantize(rows, self._quant_type, flat.shape[0], self.embedding_dim, torch.bfloat16)
         y = y.view(*x.shape, self.embedding_dim)
         if self._embed_scale is not None:
             if self._embed_scale_t is None: