Fix SM120 NVFP4 KV cache test OOM

test/registered/quant/test_llama8b_nvfp4_kv_cache_sm120.py

@@ -0,0 +1,51 @@
+import unittest
+
+from sglang.srt.utils.common import is_sm120_supported
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
+from sglang.test.server_fixtures.default_fixture import DefaultServerBase
+
+register_cuda_ci(
+    est_time=300,
+    stage="extra-a",
+    runner_config="1-gpu-small",
+    disabled="Temporarily disabled due to failing accuracy",
+)
+
+
+@unittest.skipUnless(
+    is_sm120_supported(), "requires at least 1 SM120 GPU with CUDA 12.8+"
+)
+class TestLlama8BNVFP4KVCacheSM120(GSM8KMixin, DefaultServerBase):
+    """Llama-3.1-8B-Instruct-NVFP4 with NVFP4 KV cache on SM120."""
+
+    model = "nvidia/Llama-3.1-8B-Instruct-NVFP4"
+    # Full GSM8K measured locally with 1319 requested / 1314 scored:
+    # - FP8 KV: 0.6461187214611872
+    # - NVFP4 KV: 0.632420091324201
+    # Keep the threshold 0.015 below the NVFP4 KV score.
+    gsm8k_accuracy_thres = 0.632420091324201 - 0.015
+    gsm8k_num_questions = 1319
+    gsm8k_num_threads = 200
+
+    other_args = [
+        "--quantization",
+        "modelopt_fp4",
+        "--fp4-gemm-backend",
+        "auto",
+        "--kv-cache-dtype",
+        "nvfp4",
+        "--prefill-attention-backend",
+        "flashinfer",
+        "--decode-attention-backend",
+        "trtllm_mha",
+        "--page-size",
+        "64",
+        "--mem-fraction-static",
+        "0.87",
+        "--cuda-graph-backend-prefill=disabled",
+    ]
+
+
+if __name__ == "__main__":
+    unittest.main()