diff --git a/vllm/attention/ops/chunked_prefill_paged_decode.py b/vllm/attention/ops/chunked_prefill_paged_decode.py
index 785799b6bf68..6ca2a64145bd 100644
--- a/vllm/attention/ops/chunked_prefill_paged_decode.py
+++ b/vllm/attention/ops/chunked_prefill_paged_decode.py
@@ -264,8 +264,8 @@ def chunked_prefill_paged_decode(
     # Conversion of FP8 Tensor from uint8 storage to
     # appropriate torch.dtype for interpretation by Triton
     if "fp8" in kv_cache_dtype:
-        assert key_cache.dtype == torch.uint8
-        assert value_cache.dtype == torch.uint8
+        assert key_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
+        assert value_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
 
         if kv_cache_dtype in ("fp8", "fp8_e4m3"):
             target_dtype = current_platform.fp8_dtype()
diff --git a/vllm/attention/ops/prefix_prefill.py b/vllm/attention/ops/prefix_prefill.py
index 86d256b630bf..729b61b02906 100644
--- a/vllm/attention/ops/prefix_prefill.py
+++ b/vllm/attention/ops/prefix_prefill.py
@@ -744,8 +744,8 @@ def context_attention_fwd(q,
     # Conversion of FP8 Tensor from uint8 storage to
     # appropriate torch.dtype for interpretation by Triton
     if "fp8" in kv_cache_dtype:
-        assert (k_cache.dtype == torch.uint8)
-        assert (v_cache.dtype == torch.uint8)
+        assert k_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
+        assert v_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
 
         if kv_cache_dtype in ("fp8", "fp8_e4m3"):
             target_dtype = current_platform.fp8_dtype()
diff --git a/vllm/envs.py b/vllm/envs.py
index b007bf8c59b7..bd9104afa4aa 100644
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -15,6 +15,7 @@
     VLLM_NCCL_SO_PATH: Optional[str] = None
     LD_LIBRARY_PATH: Optional[str] = None
     VLLM_USE_TRITON_FLASH_ATTN: bool = False
+    VLLM_V1_USE_PREFILL_DECODE_ATTENTION: bool = False
     VLLM_FLASH_ATTN_VERSION: Optional[int] = None
     LOCAL_RANK: int = 0
     CUDA_VISIBLE_DEVICES: Optional[str] = None
@@ -290,6 +291,13 @@ def get_vllm_port() -> Optional[int]:
     lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in
              ("true", "1")),
 
+    # Use separate prefill and decode kernels for V1 attention instead of
+    # the unified triton kernel.
+    "VLLM_V1_USE_PREFILL_DECODE_ATTENTION":
+    lambda:
+    (os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower() in
+     ("true", "1")),
+
     # Force vllm to use a specific flash-attention version (2 or 3), only valid
     # when using the flash-attention backend.
     "VLLM_FLASH_ATTN_VERSION":
@@ -323,8 +331,8 @@ def get_vllm_port() -> Optional[int]:
 
     # Whether to log responses from API Server for debugging
     "VLLM_DEBUG_LOG_API_SERVER_RESPONSE":
-    lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False").
-    lower() == "true",
+    lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
+                           ).lower() == "true",
 
     # S3 access information, used for tensorizer to load model from S3
     "S3_ACCESS_KEY_ID":
diff --git a/vllm/v1/attention/backends/triton_attn.py b/vllm/v1/attention/backends/triton_attn.py
index 4000f93984d3..a97bb85004f6 100644
--- a/vllm/v1/attention/backends/triton_attn.py
+++ b/vllm/v1/attention/backends/triton_attn.py
@@ -5,6 +5,7 @@
 import torch
 
 from vllm import _custom_ops as ops
+from vllm import envs
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType)
 from vllm.attention.ops.chunked_prefill_paged_decode import (
@@ -126,6 +127,8 @@ def __init__(
                                       "TritonAttentionImpl")
 
         self.fp8_dtype = current_platform.fp8_dtype()
+        self.force_prefill_decode_attn = \
+            envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
 
     def forward(
         self,
@@ -166,9 +169,9 @@ def forward(
         # performance to make sure it does not introduce any overhead.
 
         num_queries_per_kv = query.shape[1] // key.shape[1]
-        use_prefill_decode_attn = (num_queries_per_kv &
-                                   (num_queries_per_kv - 1)) != 0
-
+        num_q_is_pow2 = (num_queries_per_kv & (num_queries_per_kv - 1)) == 0
+        use_prefill_decode_attn = (self.force_prefill_decode_attn
+                                   or not num_q_is_pow2)
         num_actual_tokens = attn_metadata.num_actual_tokens
 
         if use_prefill_decode_attn: