diff --git a/src/host/base.jl b/src/host/base.jl
index d084b487..21e09ccb 100644
--- a/src/host/base.jl
+++ b/src/host/base.jl
@@ -1,82 +1,53 @@
 # common Base functionality
 import Base: _RepeatInnerOuter
 
-function repeat_inner_kernel!(
-    ctx::AbstractKernelContext,
-    xs::AbstractArray{<:Any, N},
-    inner::NTuple{N, Int},
-    out::AbstractArray{<:Any, N}
-) where {N}
-    # Get single element from src
-    idx = @cartesianidx xs
-    @inbounds val = xs[idx]
-
-    # Loop over "repeat" indices of inner
-    for rdx in CartesianIndices(inner)
-        # Get destination CartesianIndex
-        odx = ntuple(N) do i
-            @inbounds (idx[i]-1) * inner[i] + rdx[i]
-        end
-        @inbounds out[CartesianIndex(odx)] = val
-    end
-
-    return nothing
-end
-
-function repeat_inner(xs::AnyGPUArray, inner)
-    out = similar(xs, eltype(xs), inner .* size(xs))
-    any(==(0), size(out)) && return out # consistent with `Base.repeat`
-    gpu_call(repeat_inner_kernel!, xs, inner, out; elements=length(xs))
-    return out
+# Overload methods used by `Base.repeat`.
+# No need to implement `repeat_inner_outer` since this is implemented in `Base` as
+# `repeat_outer(repeat_inner(arr, inner), outer)`.
+function _RepeatInnerOuter.repeat_inner(xs::AnyGPUArray{<:Any, N}, inner) where {N}
+    return _repeat(xs; inner)
 end
 
-function repeat_outer_kernel!(
-    ctx::AbstractKernelContext,
-    xs::AbstractArray{<:Any, N},
-    xssize::NTuple{N},
-    outer::NTuple{N},
-    out::AbstractArray{<:Any, N}
-) where {N}
-    # Get index to input element
-    idx = @cartesianidx xs
-    @inbounds val = xs[idx]
-
-    # Loop over repeat indices, copying val to out
-    for rdx in CartesianIndices(outer)
-        # Get destination CartesianIndex
-        odx = ntuple(N) do i
-            @inbounds idx[i] + xssize[i] * (rdx[i] -1)
-        end
-        @inbounds out[CartesianIndex(odx)] = val
-    end
-
-    return nothing
+function _RepeatInnerOuter.repeat_outer(xs::AnyGPUArray{<:Any, N}, outer::NTuple{N}) where {N}
+    return _repeat(xs; outer)
 end
 
-function repeat_outer(xs::AnyGPUArray, outer)
-    out = similar(xs, eltype(xs), outer .* size(xs))
-    any(==(0), size(out)) && return out # consistent with `Base.repeat`
-    gpu_call(repeat_outer_kernel!, xs, size(xs), outer, out; elements=length(xs))
-    return out
+function _RepeatInnerOuter.repeat_inner_outer(xs::AnyGPUArray{<:Any, 1}, inner, outer)
+    return _repeat(xs; inner, outer)
 end
 
-# Overload methods used by `Base.repeat`.
-# No need to implement `repeat_inner_outer` since this is implemented in `Base` as
-# `repeat_outer(repeat_inner(arr, inner), outer)`.
-function _RepeatInnerOuter.repeat_inner(xs::AnyGPUArray{<:Any, N}, dims::NTuple{N}) where {N}
-    return repeat_inner(xs, dims)
-end
+function _repeat(x::AbstractArray, counts::Integer...)
+    N = max(ndims(x), length(counts))
+    size_y = ntuple(d -> size(x,d) * get(counts, d, 1), N)
+    size_x2 = ntuple(d -> isodd(d) ? size(x, 1+d÷2) : 1, 2*N)
 
-function _RepeatInnerOuter.repeat_outer(xs::AnyGPUArray{<:Any, N}, dims::NTuple{N}) where {N}
-    return repeat_outer(xs, dims)
-end
+    ## version without mutation
+    # ignores = ntuple(d -> reshape(Base.OneTo(counts[d]), ntuple(_->1, 2d-1)..., :), length(counts))
+    # y = reshape(broadcast(first∘tuple, reshape(x, size_x2), ignores...), size_y)
 
-function _RepeatInnerOuter.repeat_outer(xs::AnyGPUArray{<:Any, 1}, dims::Tuple{Any})
-    return repeat_outer(xs, dims)
+    # ## version with mutation
+    size_y2 = ntuple(d -> isodd(d) ? size(x, 1+d÷2) : get(counts, d÷2, 1), 2*N)
+    y = similar(x, size_y)
+    reshape(y, size_y2) .= reshape(x, size_x2)
+    y
 end
 
-function _RepeatInnerOuter.repeat_outer(xs::AnyGPUArray{<:Any, 2}, dims::NTuple{2, Any})
-    return repeat_outer(xs, dims)
+function _repeat(x::AbstractArray; inner=1, outer=1)
+    N = max(ndims(x), length(inner), length(outer))
+    size_y = ntuple(d -> size(x, d) * get(inner, d, 1) * get(outer, d, 1), N)
+    size_y3 = ntuple(3*N) do d3
+        dim, class = divrem(d3+2, 3)  # e.g. for x::Matrix, [divrem(n+2,3) for n in 1:3*2]
+        class == 0 && return get(inner, dim, 1)
+        class == 1 && return size(x, dim)
+        class == 2 && return get(outer, dim,1)
+    end
+    size_x3 = ntuple(3*N) do d3
+        dim, class = divrem(d3+2, 3)
+        class == 1 ? size(x, dim) : 1
+    end
+    y = similar(x, size_y)
+    reshape(y, size_y3) .= reshape(x, size_x3)
+    y
 end
 
 ## PermutedDimsArrays