diff --git a/ext/NNlibAMDGPUExt/NNlibAMDGPUExt.jl b/ext/NNlibAMDGPUExt/NNlibAMDGPUExt.jl
index 5e7d007e6..c9f78add1 100644
--- a/ext/NNlibAMDGPUExt/NNlibAMDGPUExt.jl
+++ b/ext/NNlibAMDGPUExt/NNlibAMDGPUExt.jl
@@ -35,12 +35,6 @@ Base.show(io::IO, x::AnyROCBatchedAdjOrTrans) = show(io, adapt(Array, x))
 
 Base.display(x::AnyROCBatchedAdjOrTrans) = display(adapt(Array, x))
 
-function NNlib._batched_gemm!(
-    ::Type{<: ROCArray}, transA::Char, transB::Char, α, A, B, β, C,
-)
-    AMDGPU.rocBLAS.gemm_batched!(transA, transB, α, A, B, β, C)
-end
-
 function nnlib_padding(dims)
     pd = NNlib.padding(dims)
     if !all(pd[1:2:end] .== pd[2:2:end])
@@ -52,6 +46,8 @@ function nnlib_padding(dims)
     pd[1:2:end]
 end
 
+include("batched_mul.jl")
+
 @static if AMDGPU.functional(:MIOpen)
     using AMDGPU.MIOpen
 
diff --git a/ext/NNlibAMDGPUExt/batched_mul.jl b/ext/NNlibAMDGPUExt/batched_mul.jl
new file mode 100644
index 000000000..191af87d1
--- /dev/null
+++ b/ext/NNlibAMDGPUExt/batched_mul.jl
@@ -0,0 +1,24 @@
+function _blas_at(x)
+    Base.stride(x, 1) == 1 && return x, 'N'
+    Base.stride(x, 2) == 1 && return batched_transpose(x), 'T'
+    throw(ArgumentError("""
+    Unsupported array layout for batched mul.
+    - Size: $(size(x))
+    - Strides: $(strides(x))
+    """))
+end
+
+function NNlib._batched_mul!(
+    ::Type{AT}, C, A, B, α::Float16, β::Float16,
+) where AT <: ROCArray{Float16}
+    blasA, transA = _blas_at(A)
+    blasB, transB = _blas_at(B)
+    NNlib._batched_gemm!(AT, transA, transB, α, blasA, blasB, β, C)
+    C
+end
+
+function NNlib._batched_gemm!(
+    ::Type{<:ROCArray{T}}, transA::Char, transB::Char, α::T, A, B, β::T, C,
+) where T <: Union{MIOPENFloat, Float64}
+    AMDGPU.rocBLAS.gemm_batched!(transA, transB, α, A, B, β, C)
+end
diff --git a/src/batched/batchedmul.jl b/src/batched/batchedmul.jl
index 9e6bef84b..a3b7efc74 100644
--- a/src/batched/batchedmul.jl
+++ b/src/batched/batchedmul.jl
@@ -223,7 +223,6 @@ _batched_mul!(::Type{DT}, C, A, B, α::Number, β::Number) where {DT<:DenseArray
     _batched_try_gemm!(DT, C, A, B, α, β)
 
 function _batched_try_gemm!(::Type{DT}, C, A, B, α::Number, β::Number) where {DT<:DenseArray{T}} where {T<:BlasFloat}
-
     alpha, beta = promote(α, β, zero(T))
     alpha isa T && beta isa T || return batched_mul_generic!(C, A, B, α, β)