diff --git a/vllm_gaudi/models/__init__.py b/vllm_gaudi/models/__init__.py
index c38c1af6f..64fad770e 100644
--- a/vllm_gaudi/models/__init__.py
+++ b/vllm_gaudi/models/__init__.py
@@ -11,3 +11,7 @@ def register_model():
     from vllm_gaudi.models.qwen2_5_vl import HpuQwen2_5_VLForConditionalGeneration  # noqa: F401
     ModelRegistry.register_model("Qwen2_5_VLForConditionalGeneration",
                                  "vllm_gaudi.models.qwen2_5_vl:HpuQwen2_5_VLForConditionalGeneration")
+
+    from vllm_gaudi.models.qwen3_vl import HpuQwen3_VLForConditionalGeneration  # noqa: F401
+    ModelRegistry.register_model("Qwen3VLForConditionalGeneration",
+                                 "vllm_gaudi.models.qwen3_vl:HpuQwen3_VLForConditionalGeneration")
diff --git a/vllm_gaudi/models/qwen2_5_vl.py b/vllm_gaudi/models/qwen2_5_vl.py
index ee697c62a..f4447fddb 100644
--- a/vllm_gaudi/models/qwen2_5_vl.py
+++ b/vllm_gaudi/models/qwen2_5_vl.py
@@ -156,6 +156,7 @@ def forward(
             rotary_pos_emb_cos: torch.Tensor,
             rotary_pos_emb_sin: torch.Tensor,
             attn_mask: Optional[torch.Tensor] = None,  # Only used for HPU
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
     ) -> torch.Tensor:
         # [s, b, c] --> [s, b, head * 3 * head_dim]
         x, _ = self.qkv(x)
diff --git a/vllm_gaudi/models/qwen3_vl.py b/vllm_gaudi/models/qwen3_vl.py
new file mode 100644
index 000000000..9dd551155
--- /dev/null
+++ b/vllm_gaudi/models/qwen3_vl.py
@@ -0,0 +1,99 @@
+from torch import nn
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.config import VllmConfig
+from vllm.model_executor.models.qwen3_vl import (
+    Qwen3VLForConditionalGeneration,
+    Qwen3_VisionTransformer,
+    Qwen3_VisionBlock,
+)
+from vllm.model_executor.models.utils import maybe_prefix
+
+from vllm_gaudi.models.qwen2_5_vl import (HPUQwen2_5_VisionAttention)
+
+
+class HPUQwen3_VisionBlock(Qwen3_VisionBlock):
+
+    def __init__(
+        self,
+        *,
+        dim: int,
+        num_heads: int,
+        mlp_hidden_dim: int,
+        act_fn,
+        norm_layer,
+        quant_config=None,
+        multimodal_config=None,
+        prefix: str = "",
+    ):
+        super().__init__(
+            dim=dim,
+            num_heads=num_heads,
+            mlp_hidden_dim=mlp_hidden_dim,
+            act_fn=act_fn,
+            norm_layer=norm_layer,
+            quant_config=quant_config,
+            multimodal_config=multimodal_config,
+            prefix=prefix,
+        )
+
+        self.attn = HPUQwen2_5_VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            quant_config=quant_config,
+            multimodal_config=multimodal_config,
+            prefix=f"{prefix}.attn",
+        )
+
+
+class HPUQwen3_VisionTransformer(Qwen3_VisionTransformer):
+
+    def __init__(
+        self,
+        vision_config,
+        norm_eps: float = 1e-6,
+        quant_config=None,
+        multimodal_config=None,
+        prefix: str = "",
+    ):
+        super().__init__(
+            vision_config=vision_config,
+            norm_eps=norm_eps,
+            quant_config=quant_config,
+            multimodal_config=multimodal_config,
+            prefix=prefix,
+        )
+
+        depth = vision_config.depth
+        norm_layer = lambda d: nn.LayerNorm(d, eps=norm_eps)
+
+        self.blocks = nn.ModuleList([
+            HPUQwen3_VisionBlock(
+                dim=self.hidden_size,
+                num_heads=self.num_heads,
+                mlp_hidden_dim=vision_config.intermediate_size,
+                act_fn=get_act_fn(vision_config.hidden_act),
+                norm_layer=norm_layer,
+                quant_config=quant_config,
+                multimodal_config=multimodal_config,
+                prefix=f"{prefix}.blocks.{layer_idx}",
+            ) for layer_idx in range(depth)
+        ])
+
+
+class HpuQwen3_VLForConditionalGeneration(Qwen3VLForConditionalGeneration):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        quant_config = getattr(self, "quant_config", None)
+        multimodal_config = getattr(vllm_config.model_config, "multimodal_config", None)
+
+        if hasattr(self, "visual") and self.visual is not None:
+            self.visual = HPUQwen3_VisionTransformer(
+                self.config.vision_config,
+                norm_eps=getattr(self.config, "rms_norm_eps", 1e-6),
+                quant_config=quant_config,
+                multimodal_config=multimodal_config,
+                prefix=maybe_prefix(prefix, "visual"),
+            )