[None][test] Add stock Qwen3.8-Flash-Next accuracy cases for FP8 and NVFP4 KV caches

Cover the two layouts where a K/V page is the size of a QSA index-K page:
TEP4 with an FP8 KV cache (one KV head per rank) and attention-DP ADP4
with an NVFP4 KV cache (two KV heads per rank), both on the NVFP4
checkpoint without MTP. The cases go into the QA function list only.

GSM8K and MMLU references were measured on GB300 with
TRTLLM_ACCURACY_NO_REFERENCE=1: 95.49/87.33 for TEP4 FP8 KV and
96.02/87.21 for ADP4 NVFP4 KV.

Signed-off-by: Wanli Jiang <35160485+Wanli-Jiang@users.noreply.github.com>

Author: Wanli-Jiang

tests/integration/defs/accuracy/references/gsm8k.yaml

@@ -311,6 +311,16 @@ Qwen/Qwen3.8-Flash-Next:
     kv_cache_quant_algo: NVFP4
     spec_dec_algo: MTP
     accuracy: 95.26
+  # FP8 KV cache, measured on the built-in implementation: TEP4 on GB300, no MTP,
+  # TRTLLM MoE, bf16 Mamba SSM cache.
+  - quant_algo: MIXED_PRECISION
+    kv_cache_quant_algo: FP8
+    accuracy: 95.49
+  # NVFP4 KV cache, measured on the built-in implementation: ADP4 on GB300, no MTP,
+  # TRTLLM MoE, bf16 Mamba SSM cache.
+  - quant_algo: MIXED_PRECISION
+    kv_cache_quant_algo: NVFP4
+    accuracy: 96.02
 moonshotai/Kimi-K2.5:
   - quant_algo: NVFP4
     kv_cache_quant_algo: FP8

tests/integration/defs/accuracy/references/mmlu.yaml

@@ -136,6 +136,16 @@ Qwen/Qwen3.8-Flash-Next:
   - quant_algo: MIXED_PRECISION
     spec_dec_algo: MTP
     accuracy: 87.57
+  # FP8 KV cache, measured on the built-in implementation: TEP4 on GB300, no MTP,
+  # TRTLLM MoE, bf16 Mamba SSM cache.
+  - quant_algo: MIXED_PRECISION
+    kv_cache_quant_algo: FP8
+    accuracy: 87.33
+  # NVFP4 KV cache, measured on the built-in implementation: ADP4 on GB300, no MTP,
+  # TRTLLM MoE, bf16 Mamba SSM cache.
+  - quant_algo: MIXED_PRECISION
+    kv_cache_quant_algo: NVFP4
+    accuracy: 87.21
 Qwen/Qwen3.5-397B-A17B:
   - quant_algo: NVFP4
     kv_cache_quant_algo: FP8

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -6043,6 +6043,50 @@ def test_nvfp4_adp4_megamoe_cutedsl_ple_offload(
                         moe_expert_parallel_size=4,
                         enable_attention_dp=True)
 
+    @skip_pre_blackwell
+    @pytest.mark.skip_less_device(4)
+    @pytest.mark.skip_less_device_memory(70000)
+    @pytest.mark.skip_less_host_memory(131072)
+    def test_nvfp4_tep4_fp8kv_trtllm_ple_offload(
+            self, monkeypatch: pytest.MonkeyPatch, mocker) -> None:
+        """NVFP4 with TEP4 and an FP8 KV cache.
+
+        Each rank holds one KV head, so an FP8 K/V page is the size of a QSA
+        index-K page.
+        """
+        self._run_evals(f"{llm_models_root()}/Qwen3.8-Flash-Next-NVFP4",
+                        tensor_parallel_size=4,
+                        moe_backend="TRTLLM",
+                        max_draft_len=None,
+                        expected_quant_algo=QuantAlgo.MIXED_PRECISION,
+                        monkeypatch=monkeypatch,
+                        mocker=mocker,
+                        moe_expert_parallel_size=4,
+                        kv_cache_dtype="fp8")
+
+    @skip_pre_blackwell
+    @pytest.mark.skip_less_device(4)
+    @pytest.mark.skip_less_device_memory(70000)
+    @pytest.mark.skip_less_host_memory(131072)
+    def test_nvfp4_adp4_nvfp4kv_trtllm_ple_offload(
+            self, monkeypatch: pytest.MonkeyPatch, mocker) -> None:
+        """NVFP4 on four GPUs with attention DP and an NVFP4 KV cache.
+
+        Each rank holds both KV heads, so an NVFP4 K/V page is the size of a
+        QSA index-K page. QSA attention runs on the regular backend for an
+        NVFP4 KV cache.
+        """
+        self._run_evals(f"{llm_models_root()}/Qwen3.8-Flash-Next-NVFP4",
+                        tensor_parallel_size=4,
+                        moe_backend="TRTLLM",
+                        max_draft_len=None,
+                        expected_quant_algo=QuantAlgo.MIXED_PRECISION,
+                        monkeypatch=monkeypatch,
+                        mocker=mocker,
+                        moe_expert_parallel_size=4,
+                        enable_attention_dp=True,
+                        kv_cache_dtype="nvfp4")
+
 
 @pytest.mark.skip_less_device_memory(80000)
 class TestQwen3_8_27B(LlmapiAccuracyTestHarness):

tests/integration/test_lists/qa/llm_function_core.txt

@@ -610,6 +610,8 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_8_Flash_Next::test_fp8_adp4_mtp3_pre
 accuracy/test_llm_api_pytorch.py::TestQwen3_8_Flash_Next::test_nvfp4_1gpu_cutedsl_ple_offload
 accuracy/test_llm_api_pytorch.py::TestQwen3_8_Flash_Next::test_nvfp4_adp4_mtp3_trtllm_ple_offload
 accuracy/test_llm_api_pytorch.py::TestQwen3_8_Flash_Next::test_nvfp4_adp4_megamoe_cutedsl_ple_offload
+accuracy/test_llm_api_pytorch.py::TestQwen3_8_Flash_Next::test_nvfp4_adp4_nvfp4kv_trtllm_ple_offload
+accuracy/test_llm_api_pytorch.py::TestQwen3_8_Flash_Next::test_nvfp4_tep4_fp8kv_trtllm_ple_offload
 accuracy/test_llm_api_pytorch.py::TestSeedOss_36B::test_auto_dtype
 accuracy/test_llm_api_pytorch.py::TestStep3p7SpeculativeDecoding::test_guided_decoding TIMEOUT (60)
 accuracy/test_llm_api_pytorch.py::TestStep3p7SpeculativeDecoding::test_step3p7_mtp TIMEOUT (60)