diff --git a/vllm_gaudi/attention/oot_mla.py b/vllm_gaudi/attention/oot_mla.py
index 403618211..5be1f1a69 100644
--- a/vllm_gaudi/attention/oot_mla.py
+++ b/vllm_gaudi/attention/oot_mla.py
@@ -157,10 +157,64 @@ def forward_impl(
 
     # during each graph execution
     def process_weights_after_loading(self, act_dtype: torch.dtype):
-        MLAAttention.process_weights_after_loading(self, act_dtype)
-        #super(MLAAttention, self).process_weights_after_loading(act_dtype)
-        self.W_UV: torch.Tensor = self.W_UV.contiguous()
-        self.W_UK_T: torch.Tensor = self.W_UK_T.contiguous()
+        # HPU-specific: when VLLM_HPU_FORCE_CHANNEL_FP8=True (default), block-quantized
+        # FP8 weights (e.g. kv_b_proj in DeepSeek-R1) are converted to channel-wise FP8.
+        # After this conversion, weight_scale_inv becomes 1D [N_out] (per-channel) but
+        # weight_block_size is not cleared. The upstream MLAAttention.process_weights_after_loading
+        # then calls scaled_dequantize with group_shape=weight_block_size, which fails
+        # because a 1D scale is incompatible with a 2D block group_shape.
+        # We handle this by directly dequantizing kv_b_proj for the HPU path.
+        kv_b_proj = self.kv_b_proj
+        weight = kv_b_proj.weight
+        weight_scale_inv = getattr(kv_b_proj, 'weight_scale_inv', None)
+
+        if weight.dtype == torch.float8_e4m3fn and weight_scale_inv is not None:
+            if weight_scale_inv.dim() == 1:
+                # Channel-wise FP8 (produced by VLLM_HPU_FORCE_CHANNEL_FP8=True):
+                # one scale per output channel; dequant via simple broadcast multiply.
+                ws = weight_scale_inv.view(-1, 1).to(act_dtype)  # [N_out, 1]
+                kv_b_proj_weight = (weight.to(act_dtype) * ws).T
+            else:
+                # Block FP8 (force_channel_fp8=False): use HPU block dequant.
+                from vllm_gaudi.extension.ops import dequant_block_fp8_weight_naive
+                orig_M = kv_b_proj.orig_M.item() if hasattr(kv_b_proj, 'orig_M') else None
+                orig_N = kv_b_proj.orig_N.item() if hasattr(kv_b_proj, 'orig_N') else None
+                kv_b_proj_weight = dequant_block_fp8_weight_naive(
+                    weight,
+                    weight_scale_inv,
+                    kv_b_proj.weight_block_size,
+                    dtype=act_dtype,
+                    original_M=orig_M,
+                    original_N=orig_N,
+                    do_unpad=(orig_M is not None),
+                ).T
+
+            assert kv_b_proj_weight.shape == (
+                self.kv_lora_rank,
+                self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+            ), (f"{kv_b_proj_weight.shape=}, "
+                f"{self.kv_lora_rank=}, {self.num_heads=}, "
+                f"{self.qk_nope_head_dim=}, {self.v_head_dim=}")
+            kv_b_proj_weight = kv_b_proj_weight.view(
+                self.kv_lora_rank,
+                self.num_heads,
+                self.qk_nope_head_dim + self.v_head_dim,
+            )
+            W_UK, W_UV = kv_b_proj_weight.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+            self.W_UV = W_UV.transpose(0, 1).contiguous()
+            self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
+
+            from vllm.model_executor.layers.attention.attention import (set_default_quant_scales,
+                                                                        should_load_quant_weights)
+            quant_method = (self.quant_config.get_quant_method(self, prefix=self.layer_name)
+                            if self.quant_config else None)
+            if not should_load_quant_weights(quant_method):
+                set_default_quant_scales(self, register_buffer=False)
+        else:
+            # Non-FP8 kv_b_proj: use upstream logic as before.
+            MLAAttention.process_weights_after_loading(self, act_dtype)
+            self.W_UV = self.W_UV.contiguous()
+            self.W_UK_T = self.W_UK_T.contiguous()
 
     # NOTE(Chendi): PR25184 using output buffer as default, which can't be used in HPU Graph,
     # so we override and always return a new tensor
diff --git a/vllm_gaudi/extension/ops.py b/vllm_gaudi/extension/ops.py
index 45885ddbb..30c4b1c9f 100644
--- a/vllm_gaudi/extension/ops.py
+++ b/vllm_gaudi/extension/ops.py
@@ -967,6 +967,10 @@ def fp8_block_linear_postprocess_weights(layer, force_channel_fp8=False):
         weight_scale_inv = weight_scale_inv.squeeze(-1)
         layer.weight.data.copy_(weight)
         layer.weight_scale_inv = torch.nn.Parameter(weight_scale_inv, requires_grad=False)
+        # Scale is now per-channel, not per-block; clear stale block size to
+        # prevent downstream code (e.g. scaled_dequantize) from using it as
+        # a group_shape that is incompatible with the 1D channel-wise scale.
+        layer.weight_block_size = None
         htorch.core.mark_step()
         return layer
     else: