diff --git a/atom/model_ops/attention_mha.py b/atom/model_ops/attention_mha.py
index 051980ff9..8953c40e4 100644
--- a/atom/model_ops/attention_mha.py
+++ b/atom/model_ops/attention_mha.py
@@ -124,12 +124,14 @@ def _can_attempt_prefill_sink_asm(self, fwd_ctx: ForwardContext) -> bool:
             return False
         if getattr(attn_metadata, "dropout_p", 0.0) != 0.0:
             return False
-        if getattr(attn_metadata, "has_cached", False):
-            return False
+        # Prefix-cache hit (has_cached) is supported: prefill_attention gathers
+        # the cached+new KV into a dense packed [total_kv, ...] tensor and the
+        # gfx1250 sink varlen ASM kernel handles bottom-right causal for
+        # sq != sk (chunked-prefill). cu_seqlens_q / cu_seqlens_k carry the
+        # per-request new-token vs cached+new lengths, so we no longer require
+        # max_seqlen_q == max_seqlen_k.
         if attn_metadata.cu_seqlens_q is None or attn_metadata.cu_seqlens_k is None:
             return False
-        if attn_metadata.max_seqlen_q != attn_metadata.max_seqlen_k:
-            return False
         return True
 
     def _can_use_prefill_sink_asm(
@@ -366,14 +368,11 @@ def rope_cache(self, q, k, v, qkv, position, fwd_ctx: ForwardContext):
                 )
             self._cache_format = "SHUFFLE" if asm_layout else "NHD"
 
-        # Prefix cache hit: gather cached KV from paged cache and concat with new tokens
-        if attn_metadata.has_cached:
-            q, k, v, k_cache, v_cache, k_scale, v_scale = (
-                self._gather_prefix_and_concat_kv(
-                    q, k, v, k_cache, v_cache, k_scale, v_scale, attn_metadata
-                )
-            )
-
+        # NOTE: on a prefix-cache hit the cached+new KV is gathered into a dense
+        # packed tensor inside prefill_attention (the ASM varlen path that needs
+        # it). The Triton path reads the paged KV cache directly, so it never
+        # gathers. Keeping the gather out of here also means dispatch_backend
+        # sees q/k with matching token counts (sq == sk).
         return q, k, v, k_cache, v_cache, k_scale, v_scale
 
     def _gather_prefix_and_concat_kv(
@@ -734,6 +733,18 @@ def prefill_attention(
 
         # variable lenth attention use key value as input
         attn_metadata = fwd_ctx.attn_metadata
+        # Prefix-cache hit: gather cached+new KV from the paged cache into a
+        # dense packed [total_kv, ...] tensor (new tokens were already written
+        # during rope_cache). flash_attn_varlen_func then attends over the full
+        # sequence; cu_seqlens_q / cu_seqlens_k carry the new vs cached+new
+        # lengths (sq < sk), which the varlen kernel handles via bottom-right
+        # causal.
+        if attn_metadata.has_cached:
+            q, k, v, k_cache, v_cache, k_scale, v_scale = (
+                self._gather_prefix_and_concat_kv(
+                    q, k, v, k_cache, v_cache, k_scale, v_scale, attn_metadata
+                )
+            )
         sliding_window = (
             (self.sliding_window, 0, 0) if self.sliding_window > 0 else (-1, -1, 0)
         )
@@ -861,6 +872,9 @@ def dispatch_backend(
         v: torch.Tensor,
     ):
         if fwd_ctx.context.is_prefill:
+            # q/k/v here still hold only the new tokens (the prefix gather happens
+            # inside prefill_attention), so the q.shape[0] == k.shape[0] check in
+            # _can_use_prefill_sink_asm is valid.
             if self._can_use_prefill_sink_asm(q, k, v, fwd_ctx):
                 return self.prefill_attention
             if envs.ATOM_USE_UNIFIED_ATTN or self.use_flash_layout: