diff --git a/moshi/moshi/server.py b/moshi/moshi/server.py
index 4d91a728..af88c95b 100644
--- a/moshi/moshi/server.py
+++ b/moshi/moshi/server.py
@@ -20,6 +20,14 @@
 import sentencepiece
 import sphn
 import torch
+import os
+
+# Disable PyTorch's dynamic compilation (dynamo)
+# This is needed because the quantized model uses custom operations
+# that aren't compatible with dynamo
+import torch._dynamo
+torch._dynamo.config.suppress_errors = True
+
 from .client_utils import log
 from .models import loaders, MimiModel, LMModel, LMGen
 from .run_inference import get_condition_tensors
diff --git a/moshi/moshi/utils/quantize.py b/moshi/moshi/utils/quantize.py
index 9682d5b6..4bfc7e05 100644
--- a/moshi/moshi/utils/quantize.py
+++ b/moshi/moshi/utils/quantize.py
@@ -22,12 +22,62 @@ def __init__(self, linear: nn.Linear):
         weight = linear.weight
         assert weight.data.dtype.is_floating_point
         assert linear.bias is None
-        CB, SCB, _ = bnbF.int8_vectorwise_quant(weight.data.to(torch.float16))  # type: ignore
-        self.weight = nn.Parameter(CB, requires_grad=False)
-        self.weight_scb = nn.Parameter(SCB, requires_grad=False)
+
+        # Check if the weight is on a meta device
+        if weight.device.type == 'meta':
+            # For meta device, we need to preserve the shape information
+            # Create tensors with the same shape as the original weight
+            # We'll use the shape information from the linear layer
+            out_features, in_features = weight.shape
+
+            # Create CB tensor with shape [out_features * 8/8, in_features]
+            # The first dimension is rounded up to a multiple of 8
+            # This matches the shape that would be produced by int8_vectorwise_quant
+            padded_out_features = ((out_features + 7) // 8) * 8
+            self.weight = nn.Parameter(
+                torch.zeros((padded_out_features, in_features),
+                           dtype=torch.int8, device='meta'),
+                requires_grad=False
+            )
+
+            # Create SCB tensor with shape [out_features]
+            self.weight_scb = nn.Parameter(
+                torch.zeros(out_features, dtype=torch.float, device='meta'),
+                requires_grad=False
+            )
+            self.is_meta = True
+        else:
+            # Normal quantization for non-meta tensors
+            CB, SCB, _ = bnbF.int8_vectorwise_quant(weight.data.to(torch.float16))  # type: ignore
+            self.weight = nn.Parameter(CB, requires_grad=False)
+            self.weight_scb = nn.Parameter(SCB, requires_grad=False)
+            self.is_meta = False
+
+    def _check_meta_status(self):
+        """Check if the weights are still meta tensors and update is_meta flag accordingly."""
+        if hasattr(self, 'is_meta') and self.is_meta:
+            # Check if the weights have been loaded (no longer on meta device)
+            if self.weight.device.type != 'meta' and self.weight.numel() > 0:
+                self.is_meta = False
+
+                # Ensure the scale tensor is float32, regardless of the model's dtype
+                if self.weight_scb.dtype != torch.float:
+                    self.weight_scb.data = self.weight_scb.data.float()
 
     def forward(self, x):
         import bitsandbytes as bnb  # type: ignore
+
+        # Update meta status based on actual tensor properties
+        self._check_meta_status()
+
+        # Check if this is a meta tensor that hasn't been properly initialized yet
+        if hasattr(self, 'is_meta') and self.is_meta:
+            # If we're still in meta mode but trying to do a forward pass,
+            # this means the weights weren't properly loaded
+            raise RuntimeError(
+                "Attempting to run forward pass with meta tensors. "
+                "The model weights need to be loaded before running inference.")
+
         state = bnb.MatmulLtState()
         state.CB = self.weight  # type: ignore
         assert isinstance(state.CB, torch.Tensor)