[CI] Trim redundant B200 test registrations (#33586)

test/registered/attention/unittests/dense/test_fa3.py

@@ -35,7 +35,6 @@ from sglang.test.kits.attention_unittest.runner_modes.split_op_runner import (
     run_dense_split_op_extend_case,
 )
 
-register_cuda_ci(est_time=25, stage="base-b", runner_config="4-gpu-b200")
 register_cuda_ci(est_time=25, stage="base-b", runner_config="1-gpu-large")
 
 

test/registered/backends/test_flashinfer_trtllm_gen_attn_backend.py

@@ -1,65 +0,0 @@
-import os
-import unittest
-from types import SimpleNamespace
-
-from sglang.srt.utils import kill_process_tree
-from sglang.test.ci.ci_register import register_cuda_ci
-from sglang.test.run_eval import run_eval
-from sglang.test.test_utils import (
-    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-    DEFAULT_URL_FOR_TEST,
-    CustomTestCase,
-    popen_launch_server,
-)
-
-register_cuda_ci(est_time=300, suite="nightly-4-gpu-b200", nightly=True)
-
-
-class TestFlashinferTrtllmGenAttnBackend(CustomTestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.model = "Qwen/Qwen3-Next-80B-A3B-Instruct"
-        cls.base_url = DEFAULT_URL_FOR_TEST
-        cls.process = popen_launch_server(
-            cls.model,
-            cls.base_url,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            env={**os.environ, "SGLANG_ENABLE_JIT_DEEPGEMM": "False"},
-            other_args=[
-                "--attention-backend",
-                "trtllm_mha",
-                "--cuda-graph-max-bs-decode",
-                "512",
-                "--tp-size",
-                "4",
-                "--ep-size",
-                "4",
-                "--mem-fraction-static",
-                "0.7",
-                "--mamba-ssm-dtype",
-                "bfloat16",
-                "--disable-radix-cache",
-            ],
-        )
-
-    @classmethod
-    def tearDownClass(cls):
-        kill_process_tree(cls.process.pid)
-
-    def test_gsm8k(self):
-        args = SimpleNamespace(
-            base_url=self.base_url,
-            model=self.model,
-            eval_name="gsm8k",
-            api="completion",
-            max_tokens=512,
-            num_examples=200,
-            num_threads=128,
-        )
-        metrics = run_eval(args)
-        print(f"{metrics=}")
-        self.assertGreater(metrics["score"], 0.93)
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/lora/test_lora_qwen3_5_35b_a3b_logprob_diff.py

@@ -37,7 +37,7 @@ from sglang.test.test_utils import CustomTestCase
 register_cuda_ci(
     est_time=110,
     stage="base-c",
-    runner_config="4-gpu-b200",
+    runner_config="4-gpu-h100",
 )
 
 BASE_MODEL = "Qwen/Qwen3.5-35B-A3B"

test/registered/lora/test_lora_qwen3_vl_30b_a3b_instruct_logprob_diff.py

@@ -37,7 +37,7 @@ from sglang.test.test_utils import CustomTestCase
 register_cuda_ci(
     est_time=110,
     stage="base-c",
-    runner_config="4-gpu-b200",
+    runner_config="4-gpu-h100",
 )
 
 BASE_MODEL = "Qwen/Qwen3-VL-30B-A3B-Instruct"

test/registered/models_e2e/test_gpt_oss_4gpu_bf16.py

@@ -4,7 +4,6 @@ from sglang.test.ci.ci_register import register_cuda_ci
 from sglang.test.gpt_oss_common import BaseTestGptOss
 
 register_cuda_ci(est_time=220, stage="base-c", runner_config="4-gpu-h100")
-register_cuda_ci(est_time=220, stage="base-c", runner_config="4-gpu-b200")
 
 
 class TestGptOss4GpuBf16(BaseTestGptOss):

test/registered/models_e2e/test_qwen35_fp4_flashinfer.py

@@ -1,83 +0,0 @@
-import unittest
-
-import torch
-
-from sglang.test.accuracy_test_runner import AccuracyTestParams
-from sglang.test.ci.ci_register import register_cuda_ci
-from sglang.test.run_combined_tests import run_combined_tests
-from sglang.test.test_utils import (
-    CustomTestCase,
-    ModelLaunchSettings,
-)
-
-register_cuda_ci(est_time=720, stage="base-c", runner_config="4-gpu-b200")
-
-QWEN35_FP4_MODEL = "nvidia/Qwen3.5-397B-A17B-NVFP4"
-ACC_THRESHOLDS = {QWEN35_FP4_MODEL: {"gsm8k": 0.95}}
-
-_cuda_major = int(torch.version.cuda.split(".")[0]) if torch.version.cuda else 0
-
-_is_sm100_cuda13 = (
-    torch.cuda.is_available()
-    and torch.cuda.get_device_capability()[0] >= 10
-    and _cuda_major >= 13
-)
-
-
-@unittest.skipUnless(_is_sm100_cuda13, "requires SM100+ GPU and CUDA 13+")
-class TestQwen35FP4FlashInfer(CustomTestCase):
-    def test_gsm8k(self):
-        base_args = [
-            "--tp-size",
-            "4",
-            "--chunked-prefill-size",
-            "2048",
-            "--mamba-scheduler-strategy",
-            "extra_buffer",
-            "--mamba-track-interval",
-            "128",
-            "--mamba-ssm-dtype",
-            "bfloat16",
-            "--max-running-requests",
-            "128",
-            "--reasoning-parser",
-            "qwen3",
-            "--attention-backend",
-            "trtllm_mha",
-            "--quantization",
-            "modelopt_fp4",
-            "--model-loader-extra-config",
-            '{"enable_multithread_load": true,"num_threads": 64}',
-            "--linear-attn-decode-backend",
-            "flashinfer",
-            "--linear-attn-prefill-backend",
-            "flashinfer",
-        ]
-
-        variants = [
-            ModelLaunchSettings(
-                QWEN35_FP4_MODEL,
-                extra_args=base_args,
-                variant="FlashInfer",
-            ),
-        ]
-
-        run_combined_tests(
-            models=variants,
-            test_name="Qwen3.5-397B-A17B-NVFP4",
-            accuracy_params=AccuracyTestParams(
-                dataset="gsm8k",
-                baseline_accuracy=ACC_THRESHOLDS[QWEN35_FP4_MODEL]["gsm8k"],
-                num_examples=200,
-                num_threads=128,
-                max_tokens=16000,
-                thinking_mode="qwen3",
-                temperature=0.6,
-                top_p=0.95,
-                top_k=20,
-            ),
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/models_e2e/test_qwen35_fp4_mtp.py

@@ -14,7 +14,7 @@ from sglang.test.test_utils import (
     popen_launch_server,
 )
 
-register_cuda_ci(est_time=740, stage="base-c", runner_config="4-gpu-b200")
+register_cuda_ci(est_time=400, stage="base-c", runner_config="4-gpu-b200")
 
 QWEN35_FP4_MODEL = "nvidia/Qwen3.5-397B-A17B-NVFP4"
 ACC_THRESHOLDS = {QWEN35_FP4_MODEL: {"gsm8k": 0.95}}
@@ -106,33 +106,5 @@ class TestQwen35FP4MTP(ReasoningTokenUsageMixin, CustomTestCase):
         _run_mtp_gsm8k(self)
 
 
-class TestQwen35FP4MTPFlashInfer(ReasoningTokenUsageMixin, CustomTestCase):
-    reasoning_parser_name = "qwen3"
-
-    @classmethod
-    def setUpClass(cls):
-        cls.model = QWEN35_FP4_MODEL
-        cls.base_url = DEFAULT_URL_FOR_TEST
-        cls.init_reasoning_token_verifier()
-        cls.process = popen_launch_server(
-            cls.model,
-            cls.base_url,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            other_args=MTP_BASE_ARGS
-            + [
-                "--linear-attn-decode-backend",
-                "flashinfer",
-                "--enforce-disable-flashinfer-allreduce-fusion",
-            ],
-        )
-
-    @classmethod
-    def tearDownClass(cls):
-        kill_process_tree(cls.process.pid)
-
-    def test_gsm8k(self):
-        _run_mtp_gsm8k(self)
-
-
 if __name__ == "__main__":
     unittest.main()

test/registered/spec/eagle/test_eagle_dp_attention.py

@@ -21,10 +21,7 @@ from sglang.test.test_utils import (
 )
 
 # EAGLE3 with DP attention (tp=2, dp=2, requires 4 GPUs).
-# Per-commit EAGLE + DP-attn coverage on CUDA is provided by
+register_cuda_ci(est_time=99, stage="base-c", runner_config="4-gpu-h100")
-# test_eagle_infer_beta_dp_attention.py (B200 4-gpu), so this H100 variant
-# is gated to extra-b only.
-register_cuda_ci(est_time=99, stage="extra-b", runner_config="4-gpu-h100")
 register_amd_ci(est_time=200, suite="stage-c-test-4-gpu-amd")
 
 

test/registered/spec/eagle/test_eagle_infer_beta_dp_attention.py

@@ -1,85 +0,0 @@
-import unittest
-from types import SimpleNamespace
-
-import requests
-
-from sglang.srt.utils import kill_process_tree
-from sglang.test.ci.ci_register import register_cuda_ci
-from sglang.test.run_eval import run_eval
-from sglang.test.test_utils import (
-    DEFAULT_MODEL_NAME_FOR_TEST_MLA,
-    DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN,
-    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-    DEFAULT_URL_FOR_TEST,
-    CustomTestCase,
-    popen_launch_server,
-)
-
-# EAGLE with DP attention on B200 (tp=2, dp=2, requires 4 B200 GPUs)
-register_cuda_ci(est_time=90, stage="base-c", runner_config="4-gpu-b200")
-
-
-def test_gsm8k(base_url: str, model: str):
-    requests.get(base_url + "/flush_cache")
-
-    args = SimpleNamespace(
-        base_url=base_url,
-        model=model,
-        eval_name="gsm8k",
-        api="completion",
-        max_tokens=512,
-        num_examples=200,
-        num_threads=128,
-    )
-    metrics = run_eval(args)
-    server_info = requests.get(base_url + "/server_info")
-    avg_spec_accept_length = server_info.json()["internal_states"][0][
-        "avg_spec_accept_length"
-    ]
-
-    print(f"{metrics=}")
-    print(f"{avg_spec_accept_length=}")
-    return metrics, avg_spec_accept_length
-
-
-class TestEagleDPAttnServerSmall(CustomTestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
-        cls.base_url = DEFAULT_URL_FOR_TEST
-        other_args = [
-            "--tp-size",
-            "2",
-            "--dp-size",
-            "2",
-            "--enable-dp-attention",
-            "--speculative-draft-model-path",
-            DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN,
-            "--speculative-algorithm",
-            "EAGLE",
-            "--speculative-num-steps",
-            "3",
-            "--speculative-eagle-topk",
-            "1",
-            "--speculative-num-draft-tokens",
-            "4",
-        ]
-        cls.process = popen_launch_server(
-            cls.model,
-            cls.base_url,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            other_args=other_args,
-        )
-
-    @classmethod
-    def tearDownClass(cls):
-        kill_process_tree(cls.process.pid)
-
-    def test_a_gsm8k(self):
-        metrics, avg_spec_accept_length = test_gsm8k(self.base_url, self.model)
-        self.assertGreater(metrics["score"], 0.62)
-        self.assertGreater(avg_spec_accept_length, 2.7)
-
-
-if __name__ == "__main__":
-    unittest.main()