diff --git a/tensorrt_llm/_torch/pyexecutor/model_engine.py b/tensorrt_llm/_torch/pyexecutor/model_engine.py
index 7c2ce12a7f4..ac12020f80b 100644
--- a/tensorrt_llm/_torch/pyexecutor/model_engine.py
+++ b/tensorrt_llm/_torch/pyexecutor/model_engine.py
@@ -2107,6 +2107,7 @@ def _apply_incremental_update_target(
             spec_metadata.gather_ids = self.gather_ids_cuda[:total_num_tokens]
             spec_metadata.num_accepted_draft_tokens = self.num_accepted_draft_tokens_cuda[:
                                                                                           num_extend_requests]
+            spec_metadata.num_generations = num_extend_requests
 
         # Determine if we're using extend_ctx mode for linear tree decoding
         num_extend_ctx_requests = 0
diff --git a/tensorrt_llm/_torch/speculative/eagle3.py b/tensorrt_llm/_torch/speculative/eagle3.py
index 0211f3e7c98..30c3d7f92cb 100644
--- a/tensorrt_llm/_torch/speculative/eagle3.py
+++ b/tensorrt_llm/_torch/speculative/eagle3.py
@@ -446,8 +446,13 @@ def forward(self,
                                       draft_model)
 
         batch_size = attn_metadata.num_seqs
-        num_contexts = attn_metadata.num_contexts
-        num_gens = batch_size - num_contexts
+        # Use spec_metadata.num_generations for the real gen/ctx split.
+        # attn_metadata.num_contexts and num_ctx_tokens may be inflated by
+        # extend_ctx mode (which treats gen draft tokens as context for attn).
+        num_gens = spec_metadata.num_generations
+        num_contexts = batch_size - num_gens
+        num_ctx_tokens = attn_metadata._seq_lens[:num_contexts].sum().item(
+        ) if num_contexts > 0 else 0
 
         raw_logits = logits
 
@@ -499,7 +504,7 @@ def forward(self,
                         (runtime_draft_len + 1)).long()
                     gather_ids_gen = (start_ids_gen +
                                       num_accepted_tokens[num_contexts:] - 1 +
-                                      attn_metadata.num_ctx_tokens)
+                                      num_ctx_tokens)
                     gather_ids = torch.concat([
                         spec_metadata.gather_ids[:num_contexts], gather_ids_gen
                     ],
@@ -609,8 +614,8 @@ def sample_and_accept_draft_tokens(
         spec_metadata: Eagle3OneModelSpecMetadata,
     ):
         batch_size = attn_metadata.num_seqs
-        num_contexts = attn_metadata.num_contexts
-        num_gens = batch_size - num_contexts
+        num_gens = spec_metadata.num_generations
+        num_contexts = batch_size - num_gens
 
         draft_tokens = spec_metadata.draft_tokens.reshape(
             num_gens, spec_metadata.runtime_draft_len)
@@ -653,7 +658,9 @@ def prepare_1st_drafter_inputs(
         spec_metadata: Eagle3OneModelSpecMetadata,
         draft_model: nn.Module,
     ):
-        num_contexts = attn_metadata.num_contexts
+        num_contexts = attn_metadata.num_seqs - spec_metadata.num_generations
+        num_ctx_tokens = attn_metadata._seq_lens[:num_contexts].sum().item(
+        ) if num_contexts > 0 else 0
         num_tokens = input_ids.shape[0]
 
         # prepare hidden states
@@ -665,7 +672,7 @@ def prepare_1st_drafter_inputs(
 
         # context
         input_ids_ctx = self._prepare_context_input_ids(
-            input_ids, attn_metadata.num_ctx_tokens, spec_metadata.gather_ids,
+            input_ids, num_ctx_tokens, spec_metadata.gather_ids,
             accepted_tokens, num_contexts)
 
         # generation
diff --git a/tensorrt_llm/_torch/speculative/interface.py b/tensorrt_llm/_torch/speculative/interface.py
index 081030f482c..2c7c33bee81 100644
--- a/tensorrt_llm/_torch/speculative/interface.py
+++ b/tensorrt_llm/_torch/speculative/interface.py
@@ -262,7 +262,13 @@ def extend_ctx(self, attention_backend: Type[AttentionBackend]):
         """
 
         if self.use_one_engine():
-            # 1-model has separate logic for handling draft tokens
+            # SM120/121 lacks XQA spec-dec cubins for non-MLA models, so
+            # one-model must fall back to treating draft tokens as context.
+            # The C++ attention layer will still use FMHA fallback for the
+            # draft model's multi-token queries (spec-dec mode remains on).
+            if get_sm_version() in (120, 121) and issubclass(
+                    attention_backend, TrtllmAttention):
+                return True
             return False
 
         xqa_supported = get_sm_version() < 120
diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt
index 38b031e778e..2df0965d0cd 100644
--- a/tests/integration/test_lists/waives.txt
+++ b/tests/integration/test_lists/waives.txt
@@ -288,7 +288,6 @@ unittest/_torch/modules/test_fused_moe.py::test_fused_moe_triton_mxfp4[True-True
 unittest/_torch/modules/test_fused_moe.py::test_fused_moe_triton_mxfp4[True-False-False-2880-2880-128] SKIP (https://nvbugs/5996776)
 unittest/_torch/modules/test_fused_moe.py::test_fused_moe_triton_mxfp4[False-True-False-2880-2880-128] SKIP (https://nvbugs/5996776)
 unittest/_torch/modules/test_fused_moe.py::test_fused_moe_triton_mxfp4[False-False-False-2880-2880-128] SKIP (https://nvbugs/5996776)
-full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[v2_kv_cache-cutlass-one_model-overlap_scheduler] SKIP (https://nvbugs/5945047)
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-pp4-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/5945081)
 full:RTXPro6000D/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=CUTLASS-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-low_precision_combine=False-torch_compile=False] SKIP (https://nvbugs/5948435)
 unittest/_torch/modeling -k "modeling_siglip" SKIP (https://nvbugs/5941242)