diff --git a/docs/references/environment_variables.md b/docs/references/environment_variables.md
index 0372106683b1..ac1b52a4b44f 100644
--- a/docs/references/environment_variables.md
+++ b/docs/references/environment_variables.md
@@ -56,8 +56,9 @@ SGLang supports various environment variables that can be used to configure its
 | `SGLANG_JIT_DEEPGEMM_COMPILE_WORKERS` | Number of workers for parallel DeepGEMM kernel compilation | `4` |
 | `SGLANG_IN_DEEPGEMM_PRECOMPILE_STAGE` | Indicator flag used during the DeepGEMM precompile script | `"false"` |
 | `SGLANG_DG_CACHE_DIR` | Directory for caching compiled DeepGEMM kernels | `~/.cache/deep_gemm` |
-| `SGL_DG_USE_NVRTC` | Use NVRTC (instead of Triton) for JIT compilation (Experimental) | `"0"` |
-| `SGL_USE_DEEPGEMM_BMM` | Use DeepGEMM for Batched Matrix Multiplication (BMM) operations | `"false"` |
+| `SGLANG_DG_USE_NVRTC` | Use NVRTC (instead of Triton) for JIT compilation (Experimental) | `"0"` |
+| `SGLANG_USE_DEEPGEMM_BMM` | Use DeepGEMM for Batched Matrix Multiplication (BMM) operations | `"false"` |
+| `SGLANG_JIT_DEEPGEMM_FAST_WARMUP` | Precompile less kernels during warmup, which reduces the warmup time from 30min to less than 3min. Might cause performance degradation during runtime. | `"false"` |
 
 ## DeepEP Configuration
 
diff --git a/python/sglang/srt/environ.py b/python/sglang/srt/environ.py
index 1d33cd411f2a..19eae882c221 100644
--- a/python/sglang/srt/environ.py
+++ b/python/sglang/srt/environ.py
@@ -334,11 +334,14 @@ class Envs:
     # DeepGemm
     SGLANG_ENABLE_JIT_DEEPGEMM = EnvBool(True)
     SGLANG_JIT_DEEPGEMM_PRECOMPILE = EnvBool(True)
+    SGLANG_JIT_DEEPGEMM_FAST_WARMUP = EnvBool(False)
     SGLANG_JIT_DEEPGEMM_COMPILE_WORKERS = EnvInt(4)
     SGLANG_IN_DEEPGEMM_PRECOMPILE_STAGE = EnvBool(False)
     SGLANG_DG_CACHE_DIR = EnvStr(os.path.expanduser("~/.cache/deep_gemm"))
     SGLANG_DG_USE_NVRTC = EnvBool(False)
     SGLANG_USE_DEEPGEMM_BMM = EnvBool(False)
+
+    # DeepSeek MHA Optimization
     SGLANG_CHUNKED_PREFIX_CACHE_THRESHOLD = EnvInt(8192)
 
     # DeepEP
diff --git a/python/sglang/srt/layers/deep_gemm_wrapper/compile_utils.py b/python/sglang/srt/layers/deep_gemm_wrapper/compile_utils.py
index 5e25e56a239c..267e6cebe7df 100644
--- a/python/sglang/srt/layers/deep_gemm_wrapper/compile_utils.py
+++ b/python/sglang/srt/layers/deep_gemm_wrapper/compile_utils.py
@@ -27,6 +27,7 @@
 _DO_COMPILE_ALL = True
 _IS_FIRST_RANK_ON_NODE = envs.SGLANG_IS_FIRST_RANK_ON_NODE.get()
 _IN_PRECOMPILE_STAGE = envs.SGLANG_IN_DEEPGEMM_PRECOMPILE_STAGE.get()
+_FAST_WARMUP = envs.SGLANG_JIT_DEEPGEMM_FAST_WARMUP.get()
 
 # Force redirect deep_gemm cache_dir
 os.environ["DG_JIT_CACHE_DIR"] = os.getenv(
@@ -44,14 +45,43 @@ def update_deep_gemm_config(gpu_id: int, server_args: ServerArgs):
     global _DO_COMPILE_ALL
     global _IS_FIRST_RANK_ON_NODE
 
-    # Generate m_max
-    m_max = 1024 * 16
-    if server_args.chunked_prefill_size < 1:
-        m_max = 1024 * 64
-    elif server_args.chunked_prefill_size > 8192:
-        m_max = server_args.chunked_prefill_size * 2
-    m_max = min(1024 * 128, m_max)
-    _BUILTIN_M_LIST = list(range(1, m_max + 1))
+    _BUILTIN_M_LIST = []
+
+    if _FAST_WARMUP:
+        # In fast warmup mode, only compile a small set of typical Ms
+
+        # First cover all the small bs to ensure decode performance
+        _BUILTIN_M_LIST += list(range(1, 1025))
+
+        # Then cover larger batch sizes with gradually increasing steps
+        # For example, when chunekd prefill size is 16384
+        # The sampled Ms would be:
+        #   1024, 1026, ... 2046 (step 2)
+        #   2048, 2052, ... 4092 (step 4)
+        #   4096, 5004, ... 8184 (step 8)
+        #   8192, 9008, ... 16384 (step 16)
+        # Totally 1024 + 1024 / 2 + 2048 / 4 + 4096 / 8 + 8192 / 16 = 3072 kernels
+        next_m, sample_step = 1024, 2
+        max_prefill_bs = (
+            min(server_args.chunked_prefill_size, 32 * 1024)
+            if server_args.chunked_prefill_size >= 1
+            else 16 * 1024
+        )
+        while next_m < max_prefill_bs:
+            _BUILTIN_M_LIST += list(range(next_m, 2 * next_m, sample_step))
+            next_m = next_m * 2
+            sample_step = sample_step * 2
+        _BUILTIN_M_LIST.append(max_prefill_bs)
+        _BUILTIN_M_LIST = sorted(list(set(_BUILTIN_M_LIST)))
+    else:
+        # When fast warmup isn't enabled, generate m_max and compile all the covered Ms.
+        m_max = 1024 * 16
+        if server_args.chunked_prefill_size < 1:
+            m_max = 1024 * 64
+        elif server_args.chunked_prefill_size > 8192:
+            m_max = server_args.chunked_prefill_size * 2
+        m_max = min(1024 * 128, m_max)
+        _BUILTIN_M_LIST += list(range(1, m_max + 1))
 
     _IS_FIRST_RANK_ON_NODE = server_args.base_gpu_id == gpu_id