diff --git a/python/sglang/srt/layers/attention/nsa/cp_shared_kv_prefetch.py b/python/sglang/srt/layers/attention/nsa/cp_shared_kv_prefetch.py
index 51207448a..a3f5a3587 100644
--- a/python/sglang/srt/layers/attention/nsa/cp_shared_kv_prefetch.py
+++ b/python/sglang/srt/layers/attention/nsa/cp_shared_kv_prefetch.py
@@ -1001,14 +1001,18 @@ class CpSharedKVMlaPrefetcher:
             )
             num_rows = int(state.staging_current_rows.numel())
             if num_rows > 0:
-                staging_rows = staging_span.view(kv_cache.dtype).reshape(
+                # Byte view on both sides: index_copy_ has no fp8 CUDA
+                # kernel, and the copy is dtype-agnostic (whole token rows).
+                staging_rows = staging_span.view(
                     (state.num_current_pages + 1) * self.page_size,
-                    *kv_cache.shape[1:],
+                    state.page_nbytes // self.page_size,
                 )
                 staging_rows.index_copy_(
                     0,
                     state.staging_current_rows,
-                    current_kv_cache[:num_rows],
+                    current_kv_cache[:num_rows]
+                    .reshape(num_rows, -1)
+                    .view(torch.uint8),
                 )
             cp_symm_barrier(
                 staging.flag_ptrs, self_rank=int(self.layout.cp_rank)
diff --git a/python/sglang/srt/layers/attention/nsa/cp_shared_kv_runtime.py b/python/sglang/srt/layers/attention/nsa/cp_shared_kv_runtime.py
index 67c888939..db7c39c5a 100644
--- a/python/sglang/srt/layers/attention/nsa/cp_shared_kv_runtime.py
+++ b/python/sglang/srt/layers/attention/nsa/cp_shared_kv_runtime.py
@@ -4876,14 +4876,18 @@ def _compose_token_kv_partial_current_v2(
         )
         num_rows = int(fill_state.staging_current_rows.numel())
         if num_rows > 0:
-            staging_rows = staging_span.view(kv_cache.dtype).reshape(
+            # Byte view on both sides: index_copy_ has no fp8 CUDA kernel,
+            # and the copy is dtype-agnostic anyway (whole token rows).
+            staging_rows = staging_span.view(
                 (int(plan.num_current_pages) + 1) * page_size,
-                *kv_cache.shape[1:],
+                kv_page_nbytes // page_size,
             )
             staging_rows.index_copy_(
                 0,
                 fill_state.staging_current_rows,
-                current_kv_cache[:num_rows],
+                current_kv_cache[:num_rows]
+                .reshape(num_rows, -1)
+                .view(torch.uint8),
             )
         _symm_barrier_and_gather_all(
             kernels=kernels,
diff --git a/test/manual/test_cp_shared_kv_compose_v2_8rank.py b/test/manual/test_cp_shared_kv_compose_v2_8rank.py
index afea40bd5..47ea1cda1 100644
--- a/test/manual/test_cp_shared_kv_compose_v2_8rank.py
+++ b/test/manual/test_cp_shared_kv_compose_v2_8rank.py
@@ -110,6 +110,8 @@ def _build_scenario(rank: int, cp_size: int, device: torch.device):
     logical_locs = logical_locs.to(device)
 
     physical_pages = (next_page // cp_size + 3)
+    # Production KV dtype: float8_e4m3fn (caught index_copy_'s missing fp8
+    # kernel) — build payloads as bytes, view as fp8.
     kv_cache = torch.zeros(
         (physical_pages * page_size, 1, kv_dim), dtype=torch.uint8, device=device
     )
@@ -154,7 +156,9 @@ def _build_scenario(rank: int, cp_size: int, device: torch.device):
         .remainder_(249)
         .to(torch.uint8)
         .to(device)
+        .view(torch.float8_e4m3fn)
     )
+    kv_cache = kv_cache.view(torch.float8_e4m3fn)
 
     prefix_slot_spans = runtime.build_batch_prefix_slot_spans(
         logical_pages=logical_pages,
@@ -347,7 +351,9 @@ def _check_prefetch_symm(rank: int, cp_size: int, device: torch.device) -> None:
         .remainder_(247)
         .to(torch.uint8)
         .to(device)
+        .view(torch.float8_e4m3fn)
     )
+    kv_cache = kv_cache.view(torch.float8_e4m3fn)
     current_k = (
         (
             torch.arange(rows, dtype=torch.int64).view(-1, 1) * 23
@@ -435,7 +441,7 @@ def _check_prefetch_symm(rank: int, cp_size: int, device: torch.device) -> None:
         end_slot=prefix_pages,
     )
     prefix_rows = rt.slot_range_to_token_slice(page_size, 0, prefix_pages)
-    dist.all_reduce(dense_kv[prefix_rows])
+    dist.all_reduce(dense_kv[prefix_rows].view(torch.uint8))
     mla_event = torch.cuda.Event()
     mla_event.record()
     mla.handles[61] = CpSharedKVMlaPrefetchHandle(
@@ -508,8 +514,10 @@ def _check_prefetch_symm(rank: int, cp_size: int, device: torch.device) -> None:
         raise AssertionError(
             f"rank{rank}: prefetch index symm mismatch at pages {bad.cpu().tolist()}"
         )
-    if not torch.equal(ref_kv, sym_kv):
-        diff = (ref_kv != sym_kv).any(dim=-1).any(dim=-1)
+    ref_kv_b = ref_kv.view(torch.uint8)
+    sym_kv_b = sym_kv.view(torch.uint8)
+    if not torch.equal(ref_kv_b, sym_kv_b):
+        diff = (ref_kv_b != sym_kv_b).any(dim=-1).any(dim=-1)
         bad = torch.nonzero(diff).reshape(-1)[:8].cpu().tolist()
         raise AssertionError(
             f"rank{rank}: prefetch mla symm mismatch at rows {bad} "
@@ -562,6 +570,8 @@ def main() -> None:
     assert torch.equal(ref_locs, v2_locs), (
         f"rank{rank}: locs mismatch legacy vs v2"
     )
+    ref_kv = ref_kv.view(torch.uint8)
+    v2_kv = v2_kv.view(torch.uint8)
     if not torch.equal(ref_kv, v2_kv):
         diff = (ref_kv != v2_kv).any(dim=-1).any(dim=-1)
         bad_rows = torch.nonzero(diff).reshape(-1)[:8].cpu().tolist()
@@ -571,7 +581,7 @@ def main() -> None:
         )
 
     # Cross-rank: every rank must hold the SAME composed buffer.
-    ref_sum = ref_kv.to(torch.float64).sum()
+    ref_sum = ref_kv.view(torch.uint8).to(torch.float64).sum()
     sums = torch.zeros(world, dtype=torch.float64, device=device)
     dist.all_gather_into_tensor(
         sums, ref_sum.reshape(1).to(device)
@@ -596,6 +606,7 @@ def main() -> None:
         symm_kv, symm_locs = _compose(
             s, layer_id=layer_id, writers=s["current_page_writer_ranks"]
         )
+        symm_kv = symm_kv.view(torch.uint8)
         torch.cuda.synchronize()
         assert torch.equal(ref_locs, symm_locs), (
             f"rank{rank} layer{layer_id} [{tag}]: symm locs mismatch"