diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml
index 598fd5762985..cc1223d4c465 100644
--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@@ -647,13 +647,31 @@ steps:
 - label: Blackwell Test
   working_dir: "/vllm-workspace/"
   gpu: b200
-  optional: true
+  # optional: true
   source_file_dependencies:
-  - csrc/
-  - vllm/
+  - csrc/quantization/fp4/
+  - csrc/attention/mla/
+  - csrc/quantization/cutlass_w8a8/moe/
+  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
+  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/fusion.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
+    # Attention
+    # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
+    - pytest -v -s tests/kernels/attention/test_flashinfer.py -k 'not num_heads2'
+    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_decode_attention.py
+    - pytest -v -s tests/kernels/test_cutlass_mla_decode.py
+    # Quantization
+    - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
+    - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
+    - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
+    - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
+    # Fusion
+    - pytest -v -s tests/compile/test_fusion_all_reduce.py
 
 #####  1 GPU test  #####
 #####  multi gpus test  #####
diff --git a/tests/compile/test_fusion_all_reduce.py b/tests/compile/test_fusion_all_reduce.py
index b394e0035c68..4c3cf6c2a10c 100644
--- a/tests/compile/test_fusion_all_reduce.py
+++ b/tests/compile/test_fusion_all_reduce.py
@@ -136,12 +136,15 @@ def ops_in_model_before(self):
 
 
 @multi_gpu_test(num_gpus=2)
-@pytest.mark.parametrize("test_model", [
-    TestAllReduceRMSNormModel,
-    TestAllReduceFusedAddRMSNormModel,
-    TestAllReduceFusedAddRMSNormStaticQuantFP8Model,
-    TestAllReduceFusedAddRMSNormStaticQuantFP4Model,
-])
+@pytest.mark.parametrize(
+    "test_model",
+    [
+        TestAllReduceRMSNormModel,
+        TestAllReduceFusedAddRMSNormModel,
+        TestAllReduceFusedAddRMSNormStaticQuantFP8Model,
+        # TODO: Enable with torch==2.8.0
+        # TestAllReduceFusedAddRMSNormStaticQuantFP4Model,
+    ])
 @pytest.mark.parametrize("batch_size", [8])
 @pytest.mark.parametrize("seq_len", [8])
 @pytest.mark.parametrize("hidden_size", [16])
diff --git a/tests/kernels/quantization/test_cutlass_scaled_mm.py b/tests/kernels/quantization/test_cutlass_scaled_mm.py
index 544e6dc19790..8730eeaaa761 100644
--- a/tests/kernels/quantization/test_cutlass_scaled_mm.py
+++ b/tests/kernels/quantization/test_cutlass_scaled_mm.py
@@ -559,8 +559,6 @@ def test_cutlass_fp8_group_gemm(num_experts: int, per_act_token: bool,
         m_a_scales = m_g if per_act_token else 1
         n_b_scales = n_g if per_out_ch else 1
 
-        print("shape:", m_g, n_g, k_g)
-
         # Create group-specific A and B (FP8) and output (FP16/FP32)
         a_g = to_fp8(torch.randn((m_g, k_g), device=device))
         b_g = to_fp8(torch.randn((n_g, k_g), device=device).t())
@@ -639,7 +637,4 @@ def test_cutlass_fp8_group_gemm(num_experts: int, per_act_token: bool,
     for g in range(num_experts):
         baseline = baseline_tensors[g]
         c = out_tensors_stacked[expert_offsets[g]:expert_offsets[g + 1]]
-        print(baseline)
-        print(c)
-        print("*")
         torch.testing.assert_close(c, baseline, rtol=1e-2, atol=5e-4)