diff --git a/test/gpu/mixed_gpu_cpu_adjoint.jl b/test/gpu/mixed_gpu_cpu_adjoint.jl
index ed061bc62..42eab336d 100644
--- a/test/gpu/mixed_gpu_cpu_adjoint.jl
+++ b/test/gpu/mixed_gpu_cpu_adjoint.jl
@@ -1,6 +1,8 @@
 using SciMLSensitivity, OrdinaryDiffEq
 using Lux, LuxCUDA, Test, Zygote, Random, LinearAlgebra, ComponentArrays
 
+const gdev = gpu_device()
+
 CUDA.allowscalar(false)
 
 H = CuArray(rand(Float32, 2, 2))
@@ -42,10 +44,10 @@ grad = Zygote.gradient(cost, p)[1]
 rng = MersenneTwister(1234)
 m = 32
 n = 16
-Z = randn(rng, Float32, (n, m)) |> gpu
+Z = randn(rng, Float32, (n, m)) |> gdev
 𝒯 = 2.0f0
 Δτ = 1.0f-1
-ca_init = [zeros(1); ones(m)] |> gpu
+ca_init = [zeros(1); ones(m)] |> gdev
 
 function f(ca, Z, t)
     a = ca[2:end]
@@ -54,7 +56,7 @@ function f(ca, Z, t)
     Ka_unit = Z' * w_unit
     z_unit = dot(abs.(Ka_unit), a_unit)
     aKa_over_z = a .* Ka_unit / z_unit
-    [sum(aKa_over_z) / m; -abs.(aKa_over_z)] |> gpu
+    [sum(aKa_over_z) / m; -abs.(aKa_over_z)] |> gdev
 end
 
 function c(Z)