diff --git a/docs/design/attention_backends.md b/docs/design/attention_backends.md
index e726d99256f5..b6cd49bce125 100644
--- a/docs/design/attention_backends.md
+++ b/docs/design/attention_backends.md
@@ -213,4 +213,4 @@ configuration.
 | `ROCM_AITER_MLA` | fp16, bf16 | `auto` | 1 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
 | `ROCM_AITER_MLA_SPARSE` | fp16, bf16 | `auto` | Any | 576 | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
 | `ROCM_AITER_TRITON_MLA` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `TRITON_MLA` | fp16, bf16 | `auto`, `bfloat16` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
+| `TRITON_MLA` | fp16, bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
diff --git a/vllm/v1/attention/backends/mla/triton_mla.py b/vllm/v1/attention/backends/mla/triton_mla.py
index f6c1790f60c8..da02363a5888 100644
--- a/vllm/v1/attention/backends/mla/triton_mla.py
+++ b/vllm/v1/attention/backends/mla/triton_mla.py
@@ -15,11 +15,11 @@
 from vllm.model_executor.layers.batch_invariant import (
     vllm_is_batch_invariant,
 )
+from vllm.platforms import current_platform
 from vllm.platforms.interface import DeviceCapability
 from vllm.v1.attention.backend import (
     AttentionLayer,
     AttentionType,
-    is_quantized_kv_cache,
 )
 from vllm.v1.attention.ops.triton_decode_attention import decode_attention_fwd
 
@@ -31,6 +31,8 @@ class TritonMLABackend(MLACommonBackend):
     supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
         "auto",
         "bfloat16",
+        "fp8",
+        "fp8_e4m3",
     ]
 
     @staticmethod
@@ -93,11 +95,6 @@ def __init__(
                 "TritonMLAImpl"
             )
 
-        if is_quantized_kv_cache(self.kv_cache_dtype):
-            raise NotImplementedError(
-                "TritonMLA V1 with FP8 KV cache not yet supported"
-            )
-
     def _flash_attn_varlen_diff_headdims(
         self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
     ):
@@ -120,8 +117,8 @@ def forward_mqa(
         assert kv_c_and_k_pe_cache.numel() > 0
         assert attn_metadata.decode is not None
 
-        if self.kv_cache_dtype.startswith("fp8"):
-            raise NotImplementedError("FP8 Triton MLA not yet supported")
+        # Determine if we're using FP8 KV cache
+        use_fp8 = self.kv_cache_dtype.startswith("fp8")
 
         if type(q) is tuple:
             q = torch.cat(q, dim=-1)
@@ -129,10 +126,15 @@ def forward_mqa(
         assert isinstance(q, torch.Tensor)
         B = q.shape[0]
         q_num_heads = q.shape[1]
+        # Cast FP8 q to bfloat16 for Blackwell compatibility (FP8 tl.dot
+        # may produce invalid instructions) and for V up-proj torch.bmm.
+        if use_fp8 and q.dtype not in (torch.float16, torch.bfloat16):
+            q = q.to(torch.bfloat16)
+        out_dtype = q.dtype
         o = torch.zeros(
-            B, q_num_heads, self.kv_lora_rank, dtype=q.dtype, device=q.device
+            B, q_num_heads, self.kv_lora_rank, dtype=out_dtype, device=q.device
         )
-        lse = torch.zeros(B, q_num_heads, dtype=q.dtype, device=q.device)
+        lse = torch.zeros(B, q_num_heads, dtype=out_dtype, device=q.device)
 
         # For batch invariance, use only 1 split to ensure deterministic reduction
         num_kv_splits = 1 if vllm_is_batch_invariant() else 4
@@ -151,12 +153,18 @@ def forward_mqa(
             device=q.device,
         )
 
+        # View as FP8 and pass scale for in-kernel dequantization.
+        if use_fp8:
+            kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.view(current_platform.fp8_dtype())
+            k_scale = layer._k_scale
+        else:
+            k_scale = None
+
         # Add a head dim of 1
         kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
         kv_c_cache = kv_c_and_k_pe_cache[..., : self.kv_lora_rank]
         PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
 
-        # Run MQA
         decode_attention_fwd(
             q,
             kv_c_and_k_pe_cache,
@@ -169,6 +177,7 @@ def forward_mqa(
             num_kv_splits,
             self.scale,
             PAGE_SIZE,
+            k_scale=k_scale,
         )
 
         return o, lse
diff --git a/vllm/v1/attention/ops/triton_decode_attention.py b/vllm/v1/attention/ops/triton_decode_attention.py
index 1ed9698c507a..daacd1e18395 100644
--- a/vllm/v1/attention/ops/triton_decode_attention.py
+++ b/vllm/v1/attention/ops/triton_decode_attention.py
@@ -9,6 +9,7 @@
 
 # Changes:
 # - Add support for page size >= 1.
+# - Add support for FP8 quantized KV cache.
 
 # Copyright 2025 vLLM Team
 # Copyright 2023-2024 SGLang Team
@@ -27,10 +28,12 @@
 """
 Memory-efficient attention for decoding.
 It supports page size >= 1.
+It supports FP8 quantized KV cache with on-the-fly dequantization.
 """
 
 import logging
 
+import torch
 from packaging import version
 
 from vllm.platforms import current_platform
@@ -64,6 +67,7 @@ def _fwd_kernel_stage1(
     Req_to_tokens,
     B_Seqlen,
     Att_Out,
+    K_scale,  # FP8 dequantization scale for K/V cache
     stride_req_to_tokens_b,
     stride_qbs,
     stride_qh,
@@ -83,6 +87,7 @@ def _fwd_kernel_stage1(
     logit_cap: tl.constexpr,
     Lk: tl.constexpr,
     Lv: tl.constexpr,
+    USE_FP8: tl.constexpr,
 ):
     cur_batch = tl.program_id(0)
     cur_head = tl.program_id(1)
@@ -129,6 +134,11 @@ def _fwd_kernel_stage1(
                 mask=(offs_n[:, None] < split_kv_end) & (mask_d[None, :]),
                 other=0.0,
             )
+            # Dequantize FP8 KV cache on-the-fly
+            if USE_FP8:
+                k_scale = tl.load(K_scale)
+                k = k.to(tl.float32) * k_scale
+
             qk = tl.sum(q[None, :] * k, 1)
             qk *= sm_scale
 
@@ -147,6 +157,9 @@ def _fwd_kernel_stage1(
                 mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
                 other=0.0,
             )
+            # Dequantize FP8 KV cache on-the-fly
+            if USE_FP8:
+                v = v.to(tl.float32) * k_scale
 
             n_e_max = tl.maximum(tl.max(qk, 0), e_max)
             re_scale = tl.exp(e_max - n_e_max)
@@ -194,6 +207,7 @@ def _decode_att_m_fwd(
     sm_scale,
     page_size,
     logit_cap,
+    k_scale=None,
 ):
     BLOCK = 64 if not is_hip_ else 8
 
@@ -213,6 +227,15 @@ def _decode_att_m_fwd(
     BLOCK_DMODEL = triton.next_power_of_2(Lk)
     BLOCK_DV = triton.next_power_of_2(Lv)
 
+    # Determine if we're using FP8 KV cache
+    use_fp8 = k_scale is not None
+
+    # Create a dummy scale tensor if not using FP8 (Triton requires valid tensor)
+    if k_scale is None:
+        k_scale = torch.empty(1, dtype=torch.float32, device=q.device)
+
+    num_stages = 1 if use_fp8 else 2
+
     _fwd_kernel_stage1[grid](
         q,
         k_buffer,
@@ -221,6 +244,7 @@ def _decode_att_m_fwd(
         Req_to_tokens,
         B_Seqlen,
         att_out,
+        k_scale,
         Req_to_tokens.stride(0),
         q.stride(0),
         q.stride(1),
@@ -239,9 +263,10 @@ def _decode_att_m_fwd(
         PAGE_SIZE=page_size,
         logit_cap=logit_cap,
         num_warps=num_warps,
-        num_stages=2,
+        num_stages=num_stages,
         Lk=Lk,
         Lv=Lv,
+        USE_FP8=use_fp8,
     )
 
 
@@ -254,6 +279,7 @@ def _fwd_grouped_kernel_stage1(
     Req_to_tokens,
     B_Seqlen,
     Att_Out,
+    K_scale,  # FP8 dequantization scale for K/V cache
     stride_req_to_tokens_b,
     stride_qbs,
     stride_qh,
@@ -276,6 +302,7 @@ def _fwd_grouped_kernel_stage1(
     logit_cap: tl.constexpr,
     Lk: tl.constexpr,
     Lv: tl.constexpr,
+    USE_FP8: tl.constexpr,
 ):
     cur_batch = tl.program_id(0)
     cur_head_id = tl.program_id(1)
@@ -336,6 +363,11 @@ def _fwd_grouped_kernel_stage1(
                 mask=(offs_n[None, :] < split_kv_end) & (mask_d[:, None]),
                 other=0.0,
             )
+            # Dequantize FP8 KV cache on-the-fly
+            if USE_FP8:
+                k_scale = tl.load(K_scale)
+                k = k.to(tl.float32) * k_scale
+
             qk = tl.dot(q, k.to(q.dtype))
             if BLOCK_DPE > 0:
                 offs_buf_kpe = (
@@ -348,6 +380,9 @@ def _fwd_grouped_kernel_stage1(
                     mask=(offs_n[None, :] < split_kv_end) & (mask_dpe[:, None]),
                     other=0.0,
                 )
+                # Dequantize FP8 KV cache on-the-fly
+                if USE_FP8:
+                    kpe = kpe.to(tl.float32) * k_scale
                 qk += tl.dot(qpe, kpe.to(qpe.dtype))
             qk *= sm_scale
 
@@ -368,6 +403,9 @@ def _fwd_grouped_kernel_stage1(
                 mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
                 other=0.0,
             )
+            # Dequantize FP8 KV cache on-the-fly
+            if USE_FP8:
+                v = v.to(tl.float32) * k_scale
 
             n_e_max = tl.maximum(tl.max(qk, 1), e_max)
             re_scale = tl.exp(e_max - n_e_max)
@@ -416,6 +454,7 @@ def _decode_grouped_att_m_fwd(
     sm_scale,
     page_size,
     logit_cap,
+    k_scale=None,
 ):
     BLOCK = 32
     Lk = k_buffer.shape[-1]
@@ -455,6 +494,18 @@ def _decode_grouped_att_m_fwd(
         extra_kargs = {"waves_per_eu": 1, "matrix_instr_nonkdim": 16, "kpack": 2}
         num_stages = 1
 
+    # Determine if we're using FP8 KV cache
+    use_fp8 = k_scale is not None
+
+    # Reduce pipeline stages for FP8 to avoid shared memory overflow
+    # from float32 intermediates during dequantization.
+    if use_fp8:
+        num_stages = 1
+
+    # Create a dummy scale tensor if not using FP8 (Triton requires valid tensor)
+    if k_scale is None:
+        k_scale = torch.empty(1, dtype=torch.float32, device=q.device)
+
     _fwd_grouped_kernel_stage1[grid](
         q,
         k_buffer,
@@ -463,6 +514,7 @@ def _decode_grouped_att_m_fwd(
         Req_to_tokens,
         B_Seqlen,
         att_out,
+        k_scale,
         Req_to_tokens.stride(0),
         q.stride(0),
         q.stride(1),
@@ -487,6 +539,7 @@ def _decode_grouped_att_m_fwd(
         num_stages=num_stages,
         Lk=Lk,
         Lv=Lv,
+        USE_FP8=use_fp8,
         **extra_kargs,
     )
 
@@ -609,6 +662,7 @@ def decode_attention_fwd_normal(
     sm_scale,
     page_size,
     logit_cap=0.0,
+    k_scale=None,
 ):
     _decode_att_m_fwd(
         q,
@@ -621,6 +675,7 @@ def decode_attention_fwd_normal(
         sm_scale,
         page_size,
         logit_cap,
+        k_scale,
     )
     _decode_softmax_reducev_fwd(
         attn_logits, q, o, lse, v_buffer, b_seq_len, num_kv_splits
@@ -640,6 +695,7 @@ def decode_attention_fwd_grouped(
     sm_scale,
     page_size,
     logit_cap=0.0,
+    k_scale=None,
 ):
     _decode_grouped_att_m_fwd(
         q,
@@ -652,6 +708,7 @@ def decode_attention_fwd_grouped(
         sm_scale,
         page_size,
         logit_cap,
+        k_scale,
     )
     _decode_softmax_reducev_fwd(
         attn_logits, q, o, lse, v_buffer, b_seq_len, num_kv_splits
@@ -671,6 +728,7 @@ def decode_attention_fwd(
     sm_scale,
     page_size=1,
     logit_cap=0.0,
+    k_scale=None,
 ):
     assert num_kv_splits == attn_logits.shape[2]
     kv_group_num = q.shape[1] // v_buffer.shape[-2]
@@ -690,6 +748,7 @@ def decode_attention_fwd(
             sm_scale,
             page_size,
             logit_cap,
+            k_scale,
         )
     else:
         # GQA/MQA/MLA
@@ -706,4 +765,5 @@ def decode_attention_fwd(
             sm_scale,
             page_size,
             logit_cap,
+            k_scale,
         )