diff --git a/lib/NonlinearSolveBase/src/linear_solve.jl b/lib/NonlinearSolveBase/src/linear_solve.jl
index 592524ec3..0836faa86 100644
--- a/lib/NonlinearSolveBase/src/linear_solve.jl
+++ b/lib/NonlinearSolveBase/src/linear_solve.jl
@@ -72,9 +72,20 @@ function construct_linear_solver(alg, linsolve, A, b, u; stats, kwargs...)
     @bb u_cache = copy(u_fixed)
     linprob = LinearProblem(A, b; u0 = u_cache, kwargs...)
 
-    # unlias here, we will later use these as caches
+    # GPU arrays require aliasing to avoid CPU memory operations
+    # Check if any of the arrays are GPU arrays
+    is_gpu_array = _is_gpu_array(A) || _is_gpu_array(b) || _is_gpu_array(u_fixed)
+    alias_A = is_gpu_array
+    alias_b = is_gpu_array
+
+    # For GPU arrays, we need to ensure we use a GPU-compatible linear solver
+    # If no linsolve is specified and we have GPU arrays, fall back to native Julia solver
+    if is_gpu_array && linsolve === nothing
+        return NativeJLLinearSolveCache(A, b, stats)
+    end
+
     lincache = init(
-        linprob, linsolve; alias = LinearAliasSpecifier(alias_A = false, alias_b = false))
+        linprob, linsolve; alias = LinearAliasSpecifier(alias_A = alias_A, alias_b = alias_b))
     return LinearSolveJLCache(lincache, linsolve, stats)
 end
 
@@ -127,3 +138,16 @@ needs_square_A(::typeof(\), ::Any) = false
 
 needs_concrete_A(::Union{Nothing, Missing}) = false
 needs_concrete_A(::typeof(\)) = true
+
+# GPU array detection utilities
+_is_gpu_array(x) = _is_gpu_array_impl(x)
+_is_gpu_array_impl(x) = false
+
+# Define GPU array detection for common GPU array types via a heuristic:
+# If the array type name contains "GPU", "Mtl", "Cu", "ROC", or "oneAPI", it's likely a GPU array
+function _is_gpu_array_impl(x::AbstractArray)
+    T = typeof(x)
+    name = string(T)
+    return contains(name, "GPU") || contains(name, "Mtl") || contains(name, "Cu") ||
+           contains(name, "ROC") || contains(name, "oneAPI")
+end