diff --git a/trellis2/modules/sparse/config.py b/trellis2/modules/sparse/config.py
index f091dbd..c7d9004 100644
--- a/trellis2/modules/sparse/config.py
+++ b/trellis2/modules/sparse/config.py
@@ -31,13 +31,14 @@ def __detect_defaults():
 
 
 def __flex_gemm_works_on_mps():
-    """Probe flex_gemm with tiny MPS convs covering both IMPLICIT_GEMM and
-    MASKED_IMPLICIT_GEMM. If the install pre-dates the device-routing fix
-    (or pre-dates the real masked kernel in round 2), one of these returns
-    a CPU tensor — fall back to the pure-PyTorch backend rather than
-    crashing inside the model on the first LayerNorm. Build tensors on CPU
-    and move to MPS because some PyTorch builds lack int/fp16 torch.zeros
-    kernels on MPS."""
+    """Probe dense, masked, and production split-k flex_gemm kernels on MPS.
+
+    If the install pre-dates the device-routing fix (or a real masked kernel),
+    one of these returns a CPU tensor or raises. Fall back to pure PyTorch
+    rather than crashing inside the model on the first LayerNorm. Build tensors
+    on CPU and move to MPS because some PyTorch builds lack int/fp16 creation
+    kernels on MPS.
+    """
     try:
         import os
         if os.environ.get('TRELLIS_DISABLE_METAL', '0') == '1':
@@ -56,10 +57,15 @@ def __flex_gemm_works_on_mps():
         weight = torch.zeros((4, 1, 1, 1, 4), dtype=torch.float16).to('mps')
         shape = torch.Size([1, 4, 1, 1, 1])
 
-        for algo in (Algorithm.IMPLICIT_GEMM, Algorithm.MASKED_IMPLICIT_GEMM):
+        for algo in (
+            Algorithm.IMPLICIT_GEMM,
+            Algorithm.MASKED_IMPLICIT_GEMM,
+            Algorithm.MASKED_IMPLICIT_GEMM_SPLITK,
+        ):
             set_algorithm(algo)
             out, _ = sparse_submanifold_conv3d(feats, coords, shape, weight)
-            if out.device.type != 'mps':
+            torch.mps.synchronize()
+            if out.device.type != 'mps' or not bool(torch.isfinite(out).all().item()):
                 return False
         return True
     except Exception: