diff --git a/flash_attn/ops/fused_dense.py b/flash_attn/ops/fused_dense.py
index 098b538..3353767 100644
--- a/flash_attn/ops/fused_dense.py
+++ b/flash_attn/ops/fused_dense.py
@@ -170,16 +170,21 @@ class ColumnParallelLinear(nn.Linear):
         process_group: ProcessGroup,
         bias: bool = True,
         sequence_parallel=True,
+        multiple_of=1,
         device=None,
         dtype=None,
     ) -> None:
         world_size = torch.distributed.get_world_size(process_group)
-        if out_features % world_size != 0:
-            raise ValueError(
-                f"out_features ({out_features}) must be divisible by " f"world_size ({world_size})"
-            )
+        if out_features % multiple_of:
+            raise ValueError(f"out_features ({out_features}) must be a multiple of {multiple_of}")
+        multiple = out_features // multiple_of
+        # We want to split @multiple across world_size, but it could be an uneven split
+        div = multiple // world_size
+        mod = multiple % world_size
+        # The first @mod ranks get @div + 1 copies, the rest get @div copies
+        local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
         super().__init__(
-            in_features, out_features // world_size, bias=bias, device=device, dtype=dtype
+            in_features, local_multiple * multiple_of, bias=bias, device=device, dtype=dtype
         )
         self.process_group = process_group
         self.sequence_parallel = sequence_parallel
@@ -205,15 +210,20 @@ class RowParallelLinear(nn.Linear):
         process_group: ProcessGroup,
         bias: bool = True,
         sequence_parallel=True,
+        multiple_of=1,
         device=None,
         dtype=None,
     ) -> None:
         world_size = torch.distributed.get_world_size(process_group)
         rank = torch.distributed.get_rank(process_group)
-        if in_features % world_size != 0:
-            raise ValueError(
-                f"in_features ({in_features}) must be divisible by " f"world_size ({world_size})"
-            )
+        if in_features % multiple_of:
+            raise ValueError(f"in_features ({in_features}) must be a multiple of {multiple_of}")
+        multiple = in_features // multiple_of
+        # We want to split @multiple across world_size, but it could be an uneven split
+        div = multiple // world_size
+        mod = multiple % world_size
+        # The first @mod ranks get @div + 1 copies, the rest get @div copies
+        local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
         # Only rank 0 will have bias
         super().__init__(
             in_features // world_size,