diff --git a/src/implementations/svd.jl b/src/implementations/svd.jl
index 9e7e6600..7dc19c8d 100644
--- a/src/implementations/svd.jl
+++ b/src/implementations/svd.jl
@@ -398,29 +398,20 @@ function svd_trunc_no_error!(A::AbstractMatrix, USVᴴ, alg::TruncatedAlgorithm{
     (Utr, Str, Vᴴtr), _ = truncate(svd_trunc!, (U, S, Vᴴ), alg.trunc)
 
     do_gauge_fix = get(alg.alg.kwargs, :fixgauge, default_fixgauge())::Bool
+    # the output matrices here are the same size as for svd_full!
     do_gauge_fix && gaugefix!(svd_trunc!, Utr, Vᴴtr)
 
     return Utr, Str, Vᴴtr
 end
 
 function svd_trunc!(A::AbstractMatrix, USVᴴ, alg::TruncatedAlgorithm{<:GPU_Randomized})
-    U, S, Vᴴ = USVᴴ
-    check_input(svd_trunc!, A, (U, S, Vᴴ), alg.alg)
-    _gpu_Xgesvdr!(A, diagview(S), U, Vᴴ; alg.alg.kwargs...)
-
-    # TODO: make sure that truncation is based on maxrank, otherwise this might be wrong
-    (Utr, Str, Vᴴtr), _ = truncate(svd_trunc!, (U, S, Vᴴ), alg.trunc)
-
+    Utr, Str, Vᴴtr = svd_trunc_no_error!(A, USVᴴ, alg)
     # normal `truncation_error!` does not work here since `S` is not the full singular value spectrum
     normS = norm(diagview(Str))
     normA = norm(A)
     # equivalent to sqrt(normA^2 - normS^2)
     # but may be more accurate
-    ϵ = sqrt((normA + normS) * (normA - normS))
-
-    do_gauge_fix = get(alg.alg.kwargs, :fixgauge, default_fixgauge())::Bool
-    do_gauge_fix && gaugefix!(svd_trunc!, Utr, Vᴴtr)
-
+    ϵ = sqrt((normA + normS) * abs(normA - normS))
     return Utr, Str, Vᴴtr, ϵ
 end
 
diff --git a/test/svd.jl b/test/svd.jl
index affe2942..bc9e8c17 100644
--- a/test/svd.jl
+++ b/test/svd.jl
@@ -25,6 +25,9 @@ for T in (BLASFloats..., GenericFloats...), m in (0, 54), n in (0, 37, m, 63)
                 CUSOLVER_Jacobi(),
             )
             TestSuite.test_svd_algs(CuMatrix{T}, (m, n), CUDA_SVD_ALGS)
+            k = 5
+            p = min(m, n) - k - 2
+            min(m, n) > k + 2 && TestSuite.test_randomized_svd(CuMatrix{T}, (m, n), (MatrixAlgebraKit.TruncatedAlgorithm(CUSOLVER_Randomized(; k, p, niters = 20), truncrank(k)),))
             if n == m
                 TestSuite.test_svd(Diagonal{T, CuVector{T}}, m)
                 TestSuite.test_svd_algs(Diagonal{T, CuVector{T}}, m, (DiagonalAlgorithm(),))
diff --git a/test/testsuite/svd.jl b/test/testsuite/svd.jl
index 800018b2..6d5799ba 100644
--- a/test/testsuite/svd.jl
+++ b/test/testsuite/svd.jl
@@ -312,3 +312,16 @@ function test_svd_trunc_algs(
         end
     end
 end
+
+function test_randomized_svd(T::Type, sz, algs; kwargs...)
+    summary_str = testargs_summary(T, sz)
+    return @testset "randomized svd_trunc! algorithm $alg $summary_str" for alg in algs
+        A = instantiate_matrix(T, sz)
+        Ac = deepcopy(A)
+        m, n = size(A)
+        minmn = min(m, n)
+        S₀ = collect(svd_vals(A))
+        U1, S1, V1ᴴ, ϵ1 = @testinferred svd_trunc(A; alg)
+        @test collect(diagview(S1))[1:alg.alg.k] ≈ S₀[1:alg.alg.k]
+    end
+end