diff --git a/python/sglang/srt/layers/attention/flashattention_backend.py b/python/sglang/srt/layers/attention/flashattention_backend.py
index 1e220b9e2..051603a87 100644
--- a/python/sglang/srt/layers/attention/flashattention_backend.py
+++ b/python/sglang/srt/layers/attention/flashattention_backend.py
@@ -27,19 +27,42 @@ from sgl_kernel.flash_attn import flash_attn_with_kvcache
 
 @dataclass
 class FlashAttentionMetadata:
-    """Metadata for decode operations to avoid redundant computations."""
+    """Metadata to be init once in the model forward pass,
+    each layer's forward pass can reuse the metadata."""
 
+    # Cumulative sequence lengths for query
     cu_seqlens_q: torch.Tensor = None
+    # Cumulative sequence lengths for key
     cu_seqlens_k: torch.Tensor = None
+    # Maximum sequence length for query
     max_seq_len_q: int = 0
+    # Maximum sequence length for key
     max_seq_len_k: int = 0
+    # Window size (typically used by Gemma)
     window_size: tuple = (-1, -1)
+    # Page table, the index of KV Cache Tables/Blocks
     page_table: torch.Tensor = None
+    # Sequence lengths for the forward batch
     cache_seqlens_int32: torch.Tensor = None
 
 
 class FlashAttentionBackend(AttentionBackend):
-    """FlashAttention backend implementation."""
+    """FlashAttention backend implementation.
+
+    Note about the init:
+    - If no spec decoding
+        - FlashAttentionBackend will be init once when the server starts.
+    - If spec decoding
+        - FlashAttentionBackend will be init once for the target worker
+        - FlashAttentionMultiStepBackend will be once for the draft worker
+            - It will spawn num_steps FlashAttentionBackend for the draft worker
+
+    Note about CUDA Graph:
+    - We only support CUDA Graph for Decode (Normal Decode and Draft Decode) and Target Verify.
+    - We don't support CUDA Graph for Extend and Draft Extend.
+    - When server init, init_cuda_graph_state will be called first and then init_cuda_graph_capture will be called.
+    - For each forward batch, init_replay_cuda_graph will be called first and then replay the graph.
+    """
 
     def __init__(
         self,
@@ -56,41 +79,42 @@ class FlashAttentionBackend(AttentionBackend):
             and model_runner.model_config.is_encoder_decoder
         ), "Sliding window and cross attention are not supported together"
 
-        # Initialize metadata
         self.forward_metadata: FlashAttentionMetadata = None
         self.max_context_len = model_runner.model_config.context_len
         self.device = model_runner.device
         self.decode_cuda_graph_metadata = {}
+        self.target_verify_metadata = {}
         self.req_to_token = model_runner.req_to_token_pool.req_to_token
         self.page_size = model_runner.page_size
         self.use_mla = (
             model_runner.model_config.attention_arch == AttentionArch.MLA
         ) and (not global_server_args_dict["disable_mla"])
         self.skip_prefill = skip_prefill
-        self.topk = topk
-        self.speculative_num_steps = speculative_num_steps
+
+        # TODO: Support Topk > 1 for FlashAttentionBackend Spec Decoding
+        assert (
+            topk <= 1
+        ), "topk must be 1 (if spec decoding) or 0 (if no spec decoding) for FlashAttentionBackend"
+
+        self.topk = 1
         self.step_id = step_id
+        self.speculative_num_steps = speculative_num_steps
 
     def init_forward_metadata(self, forward_batch: ForwardBatch):
         """Initialize forward metadata to cache repetitive calculations."""
-        # Create metadata based on forward mode
         metadata = FlashAttentionMetadata()
-
-        # Get sequence information
         seqlens_in_batch = forward_batch.seq_lens
-        # Precompute int32 version of sequence lengths
         batch_size = len(seqlens_in_batch)
         device = seqlens_in_batch.device
-
-        if forward_batch.forward_mode == ForwardMode.DECODE:
-            if self.skip_prefill:
+        if forward_batch.forward_mode.is_decode():
+            # Skip Prefill or Draft Decode
+            # Note: Draft Decode will be ran on the Draft Worker
+            if forward_batch.spec_info is not None:
                 metadata.cu_seqlens_q = torch.arange(
-                    0, batch_size * self.topk + 1, dtype=torch.int32, device=device
+                    0, batch_size + 1, dtype=torch.int32, device=device
                 )
                 seq_lens_with_decode = seqlens_in_batch + (self.step_id + 1)
-                metadata.cache_seqlens_int32 = (
-                    (seq_lens_with_decode).repeat_interleave(self.topk).to(torch.int32)
-                )
+                metadata.cache_seqlens_int32 = seq_lens_with_decode.to(torch.int32)
                 metadata.cu_seqlens_k = torch.nn.functional.pad(
                     torch.cumsum(
                         metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
@@ -103,86 +127,58 @@ class FlashAttentionBackend(AttentionBackend):
                 metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
                     forward_batch.req_pool_indices, : metadata.max_seq_len_k
                 ]
-                metadata.page_table = metadata.page_table.repeat_interleave(
-                    self.topk, dim=0
-                )
                 cache_loc = forward_batch.out_cache_loc.view(
                     self.speculative_num_steps, -1
                 ).T
-                # Calculate page table indices and cache location indices to update the page table.
-                batch_indices = torch.arange(
-                    batch_size, device=device
-                ).repeat_interleave(self.topk * (self.step_id + 1))
-                topk_indices = torch.arange(self.topk, device=device).repeat(
-                    batch_size * (self.step_id + 1)
-                )
-                row_indices = batch_indices * self.topk + topk_indices
 
-                page_table_col_base_indices = seqlens_in_batch.unsqueeze(
-                    1
-                ) + torch.arange(self.step_id + 1, device=device)
-                page_table_col_indices = page_table_col_base_indices.view(-1).repeat(
-                    self.topk
-                )
-
-                cache_loc_col_indices = torch.arange(
-                    self.step_id + 1, device=device, dtype=torch.int32
-                ).repeat(batch_size * self.topk)
-
-                metadata.page_table[row_indices, page_table_col_indices] = cache_loc[
-                    row_indices, cache_loc_col_indices
-                ].to(torch.int32)
-            else:
+                for idx, single_seq_len in enumerate(seq_lens_with_decode):
+                    real_bsz_start_idx = idx
+                    real_bsz_end_idx = idx + 1
+                    metadata.page_table[
+                        real_bsz_start_idx:real_bsz_end_idx,
+                        (single_seq_len - (self.step_id + 1)) : single_seq_len,
+                    ] = cache_loc[
+                        real_bsz_start_idx:real_bsz_end_idx, : (self.step_id + 1)
+                    ]
+            else:  # Normal Decode without Spec Decoding
                 metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
                 metadata.cu_seqlens_k = torch.nn.functional.pad(
                     torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
                 )
-                # Precompute maximum sequence length
                 metadata.max_seq_len_k = forward_batch.seq_lens_cpu.max().item()
-                # Precompute page table
                 metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
                     forward_batch.req_pool_indices, : metadata.max_seq_len_k
                 ]
                 metadata.cu_seqlens_q = torch.arange(
                     0, batch_size + 1, dtype=torch.int32, device=device
                 )
-        elif forward_batch.forward_mode == ForwardMode.TARGET_VERIFY:
+        elif forward_batch.forward_mode.is_target_verify():
+            # Note: Target Verify will be ran on the Target Worker
             draft_token_num = forward_batch.spec_info.draft_token_num
-
-            metadata.cu_seqlens_q = torch.arange(
-                0, batch_size * draft_token_num + 1, dtype=torch.int32, device=device
+            metadata.cache_seqlens_int32 = (
+                forward_batch.seq_lens + draft_token_num
+            ).to(torch.int32)
+            metadata.max_seq_len_q = draft_token_num
+            metadata.max_seq_len_k = (
+                forward_batch.seq_lens_cpu.max().item() + draft_token_num
             )
-
-            aug_seq_lens = (forward_batch.seq_lens + draft_token_num).to(torch.int32)
-            metadata.cache_seqlens_int32 = aug_seq_lens.repeat_interleave(
-                forward_batch.spec_info.draft_token_num
+            metadata.cu_seqlens_q = torch.arange(
+                0,
+                batch_size * draft_token_num + 1,
+                draft_token_num,
+                dtype=torch.int32,
+                device=device,
             )
             metadata.cu_seqlens_k = torch.nn.functional.pad(
                 torch.cumsum(metadata.cache_seqlens_int32, dim=0, dtype=torch.int32),
                 (1, 0),
             )
-            metadata.max_seq_len_k = (
-                forward_batch.seq_lens_cpu.max().item() + draft_token_num
-            )
             metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
                 forward_batch.req_pool_indices, : metadata.max_seq_len_k
-            ].repeat_interleave(draft_token_num, dim=0)
-            aug_cum_len = torch.nn.functional.pad(
-                torch.cumsum(aug_seq_lens, dim=0, dtype=torch.int32), (1, 0)
-            )
-            for idx, single_seq_len in enumerate(aug_seq_lens):
-                metadata.page_table[
-                    idx * draft_token_num : (idx + 1) * draft_token_num, :single_seq_len
-                ] *= forward_batch.spec_info.custom_mask[
-                    aug_cum_len[idx]
-                    * draft_token_num : aug_cum_len[idx + 1]
-                    * draft_token_num
-                ].view(
-                    draft_token_num, -1
-                )
+            ]
 
-            metadata.max_seq_len_q = 1
-        else:
+        elif forward_batch.forward_mode.is_extend_or_draft_extend():
+            # Normal or Draft Extend (Both of them will be ran on the Target Worker)
             metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
             metadata.cu_seqlens_k = torch.nn.functional.pad(
                 torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
@@ -208,7 +204,6 @@ class FlashAttentionBackend(AttentionBackend):
                 metadata.max_seq_len_q = metadata.max_seq_len_k
 
         # Precompute strided indices
-        # [0, page_size, 2 * page_size, ...]
         if self.page_size > 1:
             self.strided_indices = torch.arange(
                 0, metadata.page_table.shape[1], self.page_size, device=self.device
@@ -227,7 +222,6 @@ class FlashAttentionBackend(AttentionBackend):
         forward_batch: ForwardBatch,
         save_kv_cache=True,
     ):
-
         if k is not None:
             assert v is not None
             if save_kv_cache:
@@ -262,7 +256,7 @@ class FlashAttentionBackend(AttentionBackend):
 
         page_table = metadata.page_table
 
-        # # Use Flash Attention for prefill
+        # Use Flash Attention for prefill
         if not self.use_mla:
             # Do multi-head attention
             kv_cache = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)
@@ -368,7 +362,6 @@ class FlashAttentionBackend(AttentionBackend):
             if layer.sliding_window_size is not None
             else (-1, -1)
         )
-
         page_table = metadata.page_table
 
         if not self.use_mla:
@@ -437,7 +430,6 @@ class FlashAttentionBackend(AttentionBackend):
                 k_descale=layer.k_scale,
                 v_descale=layer.v_scale,
             )
-
         return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
 
     def init_cuda_graph_state(self, max_bs: int):
@@ -449,11 +441,6 @@ class FlashAttentionBackend(AttentionBackend):
         This creates fixed-size tensors that will be reused during CUDA graph replay
         to avoid memory allocations.
         """
-        if self.speculative_num_steps > 0:
-            raise NotImplementedError(
-                "FlashAttentionBackend Spec Decoding does not support CUDA graph yet, stay tuned!"
-            )
-
         self.decode_cuda_graph_metadata = {
             # Page table for token mapping (batch_size, max_context_len)
             "page_table": torch.zeros(
@@ -462,6 +449,39 @@ class FlashAttentionBackend(AttentionBackend):
                 dtype=torch.int32,
                 device=self.device,
             ),
+            "page_table_draft_decode": torch.zeros(
+                max_bs,
+                (self.max_context_len + self.page_size - 1) // self.page_size,
+                dtype=torch.int32,
+                device=self.device,
+            ),
+            "strided_indices": torch.arange(
+                0, self.max_context_len, self.page_size, device=self.device
+            ),
+            "cache_seqlens": torch.zeros(max_bs, dtype=torch.int32, device=self.device),
+            "cu_seqlens_q": torch.arange(
+                0, max_bs + 128, dtype=torch.int32, device=self.device
+            ),
+            "cu_seqlens_k": torch.zeros(
+                max_bs + 128, dtype=torch.int32, device=self.device
+            ),
+        }
+
+        self.target_verify_metadata = {
+            "page_table": torch.zeros(
+                max_bs,
+                (self.max_context_len + self.page_size - 1) // self.page_size,
+                dtype=torch.int32,
+                device=self.device,
+            ),
+            "cache_seqlens": torch.zeros(max_bs, dtype=torch.int32, device=self.device),
+            "cu_seqlens_q": torch.zeros(
+                max_bs + 128, dtype=torch.int32, device=self.device
+            ),
+            "cu_seqlens_k": torch.zeros(
+                max_bs + 128, dtype=torch.int32, device=self.device
+            ),
+            "max_seqlen_q": 0,
             "strided_indices": torch.arange(
                 0, self.max_context_len, self.page_size, device=self.device
             ),
@@ -479,27 +499,89 @@ class FlashAttentionBackend(AttentionBackend):
     ):
         """Initialize forward metadata for capturing CUDA graph."""
         metadata = FlashAttentionMetadata()
-        # Get sequence information
-        metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
-        batch_size = len(seq_lens)
         device = seq_lens.device
-        metadata.cu_seqlens_k = torch.nn.functional.pad(
-            torch.cumsum(seq_lens, dim=0, dtype=torch.int32), (1, 0)
-        )
-        # Precompute maximum sequence length
-        metadata.max_seq_len_k = seq_lens.max().item()
-        # Precompute page table
-        metadata.page_table = self.decode_cuda_graph_metadata["page_table"][
-            req_pool_indices, :
-        ]
-        if forward_mode.is_cuda_graph():
-            # Precompute cumulative sequence lengths
-            metadata.cu_seqlens_q = torch.arange(
-                0, batch_size + 1, dtype=torch.int32, device=device
+        if forward_mode.is_decode():
+            if spec_info is not None:
+                # Draft Decode
+                metadata.cu_seqlens_q = torch.arange(
+                    0, bs + 1, dtype=torch.int32, device=device
+                )
+                metadata.cache_seqlens_int32 = self.decode_cuda_graph_metadata[
+                    "cache_seqlens"
+                ][:bs]
+
+                metadata.cu_seqlens_q = self.decode_cuda_graph_metadata["cu_seqlens_q"][
+                    : bs + 1
+                ]
+
+                metadata.cu_seqlens_k = torch.nn.functional.pad(
+                    torch.cumsum(
+                        metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
+                    ),
+                    (1, 0),
+                )
+                metadata.max_seq_len_k = seq_lens.max().item() + (self.step_id + 1)
+                metadata.page_table = self.decode_cuda_graph_metadata[
+                    "page_table_draft_decode"
+                ][req_pool_indices, :]
+            else:
+                # Normal Decode
+                # Get sequence information
+                metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
+                batch_size = len(seq_lens)
+                device = seq_lens.device
+                metadata.cu_seqlens_k = torch.nn.functional.pad(
+                    torch.cumsum(seq_lens, dim=0, dtype=torch.int32), (1, 0)
+                )
+                # Precompute maximum sequence length
+                metadata.max_seq_len_k = seq_lens.max().item()
+                # Precompute page table
+                metadata.page_table = self.decode_cuda_graph_metadata["page_table"][
+                    req_pool_indices, :
+                ]
+                # Precompute cumulative sequence lengths
+                metadata.cu_seqlens_q = torch.arange(
+                    0, batch_size + 1, dtype=torch.int32, device=device
+                )
+            self.decode_cuda_graph_metadata[bs] = metadata
+        elif forward_mode.is_target_verify():
+            draft_token_num = spec_info.draft_token_num
+
+            metadata.cache_seqlens_int32 = self.target_verify_metadata["cache_seqlens"][
+                :bs
+            ]
+            metadata.cache_seqlens_int32.copy_(
+                (seq_lens + draft_token_num).to(torch.int32)
             )
-        else:
-            raise ValueError("Do not support Prefill Mode cuda graph")
-        self.decode_cuda_graph_metadata[bs] = metadata
+
+            metadata.max_seq_len_q = draft_token_num
+            metadata.max_seq_len_k = seq_lens.max().item() + draft_token_num
+
+            metadata.cu_seqlens_q = self.target_verify_metadata["cu_seqlens_q"][
+                torch.arange(
+                    0,
+                    bs * draft_token_num + 1,
+                    draft_token_num,
+                    dtype=torch.int32,
+                    device=device,
+                )
+            ]
+            cu_k = self.target_verify_metadata["cu_seqlens_k"][: (bs + 1)]
+            cu_k.copy_(
+                torch.nn.functional.pad(
+                    torch.cumsum(
+                        metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
+                    ),
+                    (1, 0),
+                )
+            )
+            metadata.cu_seqlens_k = cu_k
+            metadata.page_table = self.target_verify_metadata["page_table"][
+                req_pool_indices, :
+            ]
+
+            self.target_verify_metadata[bs] = metadata
+
         self.forward_metadata = metadata
 
     def init_forward_metadata_replay_cuda_graph(
@@ -512,28 +594,91 @@ class FlashAttentionBackend(AttentionBackend):
         forward_mode: ForwardMode,
         spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
         seq_lens_cpu: Optional[torch.Tensor],
+        out_cache_loc: torch.Tensor = None,
     ):
         # """Initialize forward metadata for replaying CUDA graph."""
-        metadata = self.decode_cuda_graph_metadata[bs]
+        device = seq_lens.device
+        seq_lens = seq_lens[:bs]
+        req_pool_indices = req_pool_indices[:bs]
+        seq_lens_cpu = seq_lens_cpu[:bs]
+        if forward_mode.is_decode():
+            metadata = self.decode_cuda_graph_metadata[bs]
 
-        # For CPU operations
-        max_len = seq_lens_cpu[:bs].max().item()
-        metadata.max_seq_len_k = max_len
+            if spec_info is not None:
+                # Draft Decode
+                max_len = seq_lens_cpu.max().item()
+                metadata.max_seq_len_k = max_len + (self.step_id + 1)
 
-        # For GPU operations
-        seq_lens_in_batch = seq_lens[:bs]
-        metadata.cache_seqlens_int32 = seq_lens_in_batch.to(torch.int32)
-        metadata.cu_seqlens_k = torch.nn.functional.pad(
-            torch.cumsum(seq_lens_in_batch, dim=0, dtype=torch.int32), (1, 0)
-        )
+                metadata.cache_seqlens_int32.copy_(
+                    (seq_lens + (self.step_id + 1)).to(torch.int32)
+                )
+
+                metadata.max_seq_len_k = seq_lens_cpu.max().item() + (self.step_id + 1)
+
+                metadata.cu_seqlens_k.copy_(
+                    torch.nn.functional.pad(
+                        torch.cumsum(
+                            metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
+                        ),
+                        (1, 0),
+                    )
+                )
+
+                page_table = self.req_to_token[
+                    req_pool_indices, : metadata.max_seq_len_k
+                ]
+
+                metadata.page_table[:, : metadata.max_seq_len_k].copy_(page_table)
+            else:
+                # Normal Decode
+                max_len = seq_lens_cpu.max().item()
+                metadata.max_seq_len_k = max_len
+
+                metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
+                metadata.cu_seqlens_k = torch.nn.functional.pad(
+                    torch.cumsum(seq_lens, dim=0, dtype=torch.int32), (1, 0)
+                )
+
+                max_seq_pages = (
+                    metadata.max_seq_len_k + self.page_size - 1
+                ) // self.page_size
+                page_indices = self.req_to_token[
+                    :,
+                    self.decode_cuda_graph_metadata["strided_indices"][:max_seq_pages],
+                ]
+                page_indices = page_indices[req_pool_indices] // self.page_size
+                metadata.page_table[:, :max_seq_pages].copy_(page_indices)
+                metadata.page_table[:, max_seq_pages:].fill_(0)
+
+        elif forward_mode.is_target_verify():
+            metadata = self.target_verify_metadata[bs]
+            draft_token_num = spec_info.draft_token_num
+
+            metadata.cu_seqlens_q.copy_(
+                torch.arange(
+                    0,
+                    bs * draft_token_num + 1,
+                    draft_token_num,
+                    dtype=torch.int32,
+                    device=device,
+                )
+            )
+            metadata.cache_seqlens_int32.copy_(
+                (seq_lens + draft_token_num).to(torch.int32)
+            )
+
+            metadata.max_seq_len_k = seq_lens_cpu.max().item() + draft_token_num
+            metadata.cu_seqlens_k.copy_(
+                torch.nn.functional.pad(
+                    torch.cumsum(
+                        metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
+                    ),
+                    (1, 0),
+                )
+            )
+            page_table = self.req_to_token[req_pool_indices, : metadata.max_seq_len_k]
+            metadata.page_table[:, : metadata.max_seq_len_k].copy_(page_table)
 
-        max_seq_pages = (metadata.max_seq_len_k + self.page_size - 1) // self.page_size
-        page_indices = self.req_to_token[
-            :, self.decode_cuda_graph_metadata["strided_indices"][:max_seq_pages]
-        ]
-        page_indices = page_indices[req_pool_indices[:bs]] // self.page_size
-        metadata.page_table[:, :max_seq_pages].copy_(page_indices)
-        metadata.page_table[:, max_seq_pages:].fill_(0)
         self.forward_metadata = metadata
 
     def get_cuda_graph_seq_len_fill_value(self):
@@ -555,7 +700,6 @@ class FlashAttentionMultiStepBackend:
             self.attn_backends.append(
                 FlashAttentionBackend(
                     model_runner,
-                    skip_prefill=True,
                     topk=self.topk,
                     speculative_num_steps=self.speculative_num_steps,
                     step_id=i,
@@ -570,7 +714,10 @@ class FlashAttentionMultiStepBackend:
         for i in range(self.speculative_num_steps):
             self.attn_backends[i].init_cuda_graph_state(max_bs)
 
-    def init_forward_metadata_capture_cuda_graph(self, forward_batch: ForwardBatch):
+    def init_forward_metadata_capture_cuda_graph(
+        self,
+        forward_batch: ForwardBatch,
+    ):
         assert forward_batch.spec_info is not None
         assert isinstance(forward_batch.spec_info, EagleDraftInput)
 
@@ -601,4 +748,5 @@ class FlashAttentionMultiStepBackend:
                 forward_mode=ForwardMode.DECODE,
                 spec_info=forward_batch.spec_info,
                 seq_lens_cpu=forward_batch.seq_lens_cpu,
+                out_cache_loc=forward_batch.out_cache_loc,
             )
diff --git a/python/sglang/srt/model_executor/forward_batch_info.py b/python/sglang/srt/model_executor/forward_batch_info.py
index daed3dc29..96a13a999 100644
--- a/python/sglang/srt/model_executor/forward_batch_info.py
+++ b/python/sglang/srt/model_executor/forward_batch_info.py
@@ -104,6 +104,9 @@ class ForwardMode(IntEnum):
             or self == ForwardMode.IDLE
         )
 
+    def is_extend_or_draft_extend(self):
+        return self == ForwardMode.EXTEND or self == ForwardMode.DRAFT_EXTEND
+
     def is_dummy_first(self):
         return self == ForwardMode.DUMMY_FIRST