diff --git a/python/sglang/srt/layers/attention/dsv4/indexer.py b/python/sglang/srt/layers/attention/dsv4/indexer.py
index 8db3b0fc8..d86f59038 100644
--- a/python/sglang/srt/layers/attention/dsv4/indexer.py
+++ b/python/sglang/srt/layers/attention/dsv4/indexer.py
@@ -22,6 +22,7 @@ from sglang.srt.layers.attention.dsv4.metadata import (
     PagedIndexerMetadata,
 )
 from sglang.srt.layers.linear import ReplicatedLinear
+from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph.context import (
     is_in_breakable_cuda_graph,
@@ -44,12 +45,8 @@ if TYPE_CHECKING:
     from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 
-if is_hip():
-    FP8_DTYPE = torch.float8_e4m3fnuz
-    FP8_MAX = torch.finfo(FP8_DTYPE).max
-else:
-    FP8_DTYPE = torch.float8_e4m3fn
-    FP8_MAX = torch.finfo(FP8_DTYPE).max
+FP8_DTYPE = torch.float8_e4m3fnuz if is_fp8_fnuz() else torch.float8_e4m3fn
+
 
 IndexerQuery: TypeAlias = Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
 
diff --git a/python/sglang/srt/layers/attention/dsv4/unified_kv_kernels/paged_decode.py b/python/sglang/srt/layers/attention/dsv4/unified_kv_kernels/paged_decode.py
index 6a71caaaa..e04338275 100644
--- a/python/sglang/srt/layers/attention/dsv4/unified_kv_kernels/paged_decode.py
+++ b/python/sglang/srt/layers/attention/dsv4/unified_kv_kernels/paged_decode.py
@@ -58,12 +58,15 @@ import triton
 import triton.language as tl
 from aiter.ops.triton.utils.device_info import get_num_sms
 
+from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
+
 LOG2E = 1.4426950408889634  # log2(e); folded into qk_scale so softmax can use exp2.
 _MAX_KV_SPLITS = 64  # Hard cap on kv_splits (see _kv_splits_heuristic).
 
 # FP8 KV cache (1xGROUP_SIZE block-scale quantization).
 #
-# Storage: unified_kv[total_pages, D] in e4m3fnuz + kv_scales[total_pages,
+# Storage: unified_kv[total_pages, D] in e4m3fnuz (gfx942 only) / e4m3fn +
+# kv_scales[total_pages,
 # D // GROUP_SIZE] in fp32. Per-slot, D is split into NUM_GROUPS chunks of
 # GROUP_SIZE elements; each chunk shares one fp32 scale.
 # Dequant in-kernel: kv_bf16 = kv_fp8.to(fp32) * scale[d // GROUP_SIZE], cast
@@ -73,7 +76,9 @@ _MAX_KV_SPLITS = 64  # Hard cap on kv_splits (see _kv_splits_heuristic).
 # per slot, 4 bytes each → +6.25% storage on top of the fp8 pool (vs the
 # halving from bf16→fp8 = 2× saving — net ~46% read bandwidth reduction).
 _FP8_GROUP_SIZE = 64
-_FP8_DTYPE = torch.float8_e4m3fnuz
+# Match the KV-cache write format: fnuz only on AMD gfx942; Other platform e.g.
+# gfx950 store e4m3fn. See quant_k_cache.py / indexer.py FP8_DTYPE.
+_FP8_DTYPE = torch.float8_e4m3fnuz if is_fp8_fnuz() else torch.float8_e4m3fn
 
 
 @functools.lru_cache(maxsize=1)