[None][test] Add func and perf cases for Qwen3.6-35B-A3B and gemma4 on Spark

tests/integration/defs/accuracy/test_llm_api_pytorch_multimodal.py

@@ -33,7 +33,6 @@
 
 from ..conftest import (
     get_sm_version,
-    is_sm_100f,
     llm_models_root,
     skip_post_blackwell_ultra,
     skip_pre_blackwell,
@@ -228,10 +227,7 @@ def test_nvfp4_prequantized(self):
             task.evaluate(llm, sampling_params=self.sampling_params)
 
 
-@pytest.mark.skipif(
-    not is_sm_100f(),
-    reason="Gemma4 shared-KV MTP requires FlashInfer trtllm-gen on the SM100 family",
-)
+@pytest.mark.skip_device_not_contain(["B200", "GB10"])
 class TestGemma4_26B_A4B(LlmapiAccuracyTestHarness):
     MODEL_NAME = "google/gemma-4-26B-A4B-it"
     MODEL_PATH = f"{llm_models_root()}/gemma/nvidia-Gemma-4-26B-A4B-NVFP4"
@@ -274,6 +270,22 @@ def test_nvfp4(self):
                 extra_evaluator_kwargs=self.EXTRA_EVALUATOR_KWARGS,
             )
 
+    def test_nvfp4_no_mtp(self):
+        # Same checkpoint without MTP drafting.
+        with LLM(
+            self.MODEL_PATH,
+            max_batch_size=16,
+            kv_cache_config=self.kv_cache_config,
+            enable_chunked_prefill=True,
+        ) as llm:
+            assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4
+            task = MMMU(self.MODEL_NAME)
+            task.evaluate(
+                llm,
+                sampling_params=self.sampling_params,
+                extra_evaluator_kwargs=self.EXTRA_EVALUATOR_KWARGS,
+            )
+
 
 class TestQwen3VL_MOE(LlmapiAccuracyTestHarness):
     MODEL_NAME = "Qwen/Qwen3-VL-30B-A3B-Instruct"

tests/integration/defs/perf/_model_paths.py

@@ -68,8 +68,10 @@
     "qwen3.5_122b_a10b": "Qwen3.5-122B-A10B",
     "qwen3.5_397b_a17b_fp8": "Qwen3.5-397B-A17B-FP8",
     "qwen3.5_397b_a17b_fp4": "Qwen3.5-397B-A17B-NVFP4",
-    # Qwen3.6 (GDN linear-attn MoE, NVFP4)
+    # Qwen3.6 (GDN linear-attn MoE, NVFP4). The _mtp key is the same checkpoint
+    # benchmarked with one-model MTP drafting, so the two can be compared.
     "qwen3.6_35b_a3b_fp4": "Qwen3.6-35B-A3B-NVFP4",
+    "qwen3.6_35b_a3b_fp4_mtp": "Qwen3.6-35B-A3B-NVFP4",
     # DeepSeek V4
     "deepseek_v4_pro_fp4": "DeepSeek-V4-Pro",
     "deepseek_v4_flash": "DeepSeek-V4-Flash",

tests/integration/defs/perf/pytorch_model_config.py

@@ -314,6 +314,28 @@ def get_model_yaml_config(model_label: str,
                 },
             }
         },
+        # Qwen3.6-35B-A3B NVFP4 one-model MTP drafting
+        {
+            'patterns': ['qwen3.6_35b_a3b_fp4_mtp-bench-pytorch-streaming'],
+            'config': {
+                'trust_remote_code': True,
+                'moe_config': {
+                    'backend': 'CUTLASS',
+                },
+                'enable_chunked_prefill': True,
+                'cuda_graph_config': {
+                    'enable_padding': True,
+                },
+                'kv_cache_config': {
+                    'enable_block_reuse': False,
+                    'dtype': 'fp8',
+                },
+                'speculative_config': {
+                    'decoding_type': 'MTP',
+                    'max_draft_len': 1,
+                },
+            }
+        },
         # MiniMax-M3 MXFP8 block-sparse MoE: sparse backend, no KV reuse, trust_remote_code, capped max_seq_len to avoid the 1M-default CUDA-graph OOM.
         {
             'patterns': ['minimax_m3_mxfp8'],

tests/integration/defs/perf/test_perf.py

@@ -63,6 +63,7 @@
     "glm_5_fp8",
     "minimax_m3_mxfp8",
     "qwen3.6_35b_a3b_fp4",
+    "qwen3.6_35b_a3b_fp4_mtp",
     "nemotron_3_nano_omni_nvfp4",
     "nemotron_3_nano_omni_nvfp4_image",
     "nemotron_nano_12b_v2",
@@ -96,6 +97,7 @@
     "qwen3_235b_a22b_fp4_eagle3",
     "gpt_oss_120b_eagle3",
     "gpt_oss_120b_eagle3_throughput",
+    "qwen3.6_35b_a3b_fp4_mtp",
     *SPEC_DEC_REAL_DATASET_MODELS,
 }
 

tests/integration/defs/test_e2e.py

@@ -916,6 +916,9 @@ def test_ptp_quickstart(llm_root, llm_venv):
     pytest.param('Nemotron-Nano-9B-v2-nvfp4',
                  'NVIDIA-Nemotron-Nano-9B-v2-NVFP4',
                  marks=skip_pre_blackwell),
+    pytest.param('Qwen3.6-35B-A3B-nvfp4',
+                 'Qwen3.6-35B-A3B-NVFP4',
+                 marks=skip_pre_blackwell),
 ])
 def test_ptp_quickstart_advanced(llm_root, llm_venv, model_name, model_path):
     print(f"Testing {model_name}.")
@@ -924,7 +927,9 @@ def test_ptp_quickstart_advanced(llm_root, llm_venv, model_name, model_path):
         llm_venv.run_cmd([
             str(example_root / "quickstart_advanced.py"),
             "--disable_kv_cache_reuse",
+            "--trust_remote_code",
             "--max_batch_size=8",
+            "--use_kv_cache_manager_v2=false",
             "--model_dir",
             f"{llm_models_root()}/{model_path}",
         ])
@@ -943,6 +948,10 @@ def test_ptp_quickstart_advanced(llm_root, llm_venv, model_name, model_path):
         ]
         if "Qwen3" in model_name:
             cmds.append("--kv_cache_fraction=0.6")
+        if "Qwen3.6-35B-A3B" in model_name:
+            # Hybrid linear-attention model: the Mamba cache preallocates a
+            # recurrent state per sequence slot, so cap the batch size.
+            cmds.append("--max_batch_size=1")
         llm_venv.run_cmd(cmds)
 
 

tests/integration/test_lists/qa/llm_spark_func.yml

@@ -28,13 +28,16 @@ llm_spark_func:
   - test_e2e.py::test_ptp_quickstart_advanced[Qwen3-30B-A3B_nvfp4_hf-Qwen3/saved_models_Qwen3-30B-A3B_nvfp4_hf]
   - test_e2e.py::test_ptp_quickstart_advanced[Nemotron-Nano-9B-v2-nvfp4-NVIDIA-Nemotron-Nano-9B-v2-NVFP4]
   - test_e2e.py::test_ptp_quickstart_advanced_eagle3[GPT-OSS-120B-Eagle3-gpt_oss/gpt-oss-120b-gpt_oss/gpt-oss-120b-Eagle3]
+  - test_e2e.py::test_ptp_quickstart_advanced[Qwen3.6-35B-A3B-nvfp4-Qwen3.6-35B-A3B-NVFP4]
   - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B::test_auto_dtype
   - accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B::test_nvfp4
   - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_fp8_prequantized
   - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_27BInstruct::test_nvfp4_prequantized
   - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_12BInstruct::test_auto_dtype
   - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_12BInstruct::test_fp8_prequantized
   - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma3_12BInstruct::test_nvfp4_prequantized
+  - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma4_26B_A4B::test_nvfp4_no_mtp
+  - accuracy/test_llm_api_pytorch_multimodal.py::TestGemma4_26B_A4B::test_nvfp4
   - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_cutlass-torch_compile=False]
   - accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[latency_moe_cutlass-torch_compile=True]
   - test_e2e.py::test_trtllm_benchmark_serving[gpt_oss/gpt-oss-20b]

tests/integration/test_lists/qa/llm_spark_perf.yml

@@ -44,6 +44,10 @@ llm_spark_perf:
   - perf/test_perf.py::test_perf[gemma_3_12b_it-bench-pytorch-streaming-bfloat16-maxbs:1-input_output_len:2048,128-reqs:1-con:1]
   - perf/test_perf.py::test_perf[gemma_3_12b_it_fp8-bench-pytorch-streaming-float8-maxbs:1-input_output_len:2048,128-reqs:1-con:1]
   - perf/test_perf.py::test_perf[gemma_3_12b_it_fp4-bench-pytorch-streaming-float4-maxbs:1-input_output_len:2048,128-reqs:1-con:1]
+  - perf/test_perf.py::test_perf[gemma_4_26b_a4b_nvfp4-bench-pytorch-streaming-float4-maxbs:1-input_output_len:2048,128-reqs:1-con:1]
+  # Qwen3.6-35B-A3B NVFP4: baseline and the same checkpoint with MTP drafting
+  - perf/test_perf.py::test_perf[qwen3.6_35b_a3b_fp4-bench-pytorch-streaming-float4-maxbs:1-input_output_len:2048,256-reqs:1-con:1]
+  - perf/test_perf.py::test_perf[qwen3.6_35b_a3b_fp4_mtp-bench-pytorch-streaming-float4-maxbs:1-input_output_len:2048,256-reqs:1-con:1]
 # ===============================================================================
 # 2: Multi-GPU (2 GPUs) Spark perf cases with multinode support
 # ===============================================================================