diff --git a/lib/cusolver/dense_generic.jl b/lib/cusolver/dense_generic.jl
index 90995be7d2..93997424b5 100644
--- a/lib/cusolver/dense_generic.jl
+++ b/lib/cusolver/dense_generic.jl
@@ -190,6 +190,7 @@ end
 
 # Xlarft!
 function larft!(direct::Char, storev::Char, v::StridedCuMatrix{T}, tau::StridedCuVector{T}, t::StridedCuMatrix{T}) where {T <: BlasFloat}
+    CUSOLVER.version() < v"11.6.0" && throw(ErrorException("This operation is not supported by the current CUDA version."))
     n, k = size(v)
     ktau = length(tau)
     mt, nt = size(t)
@@ -449,6 +450,7 @@ end
 
 # Xgeev
 function Xgeev!(jobvl::Char, jobvr::Char, A::StridedCuMatrix{T}) where {T <: BlasFloat}
+    CUSOLVER.version() < v"11.7.1" && throw(ErrorException("This operation is not supported by the current CUDA version."))
     n = checksquare(A)
     VL = if jobvl == 'V'
         CuMatrix{T}(undef, n, n)
@@ -492,6 +494,44 @@ function Xgeev!(jobvl::Char, jobvr::Char, A::StridedCuMatrix{T}) where {T <: Bla
     return W, VL, VR
 end
 
+# XsyevBatched
+function XsyevBatched!(jobz::Char, uplo::Char, A::StridedCuMatrix{T}) where {T <: BlasFloat}
+    CUSOLVER.version() < v"11.7.1" && throw(ErrorException("This operation is not supported by the current CUDA version."))
+    chkuplo(uplo)
+    n, num_matrices = size(A)
+    batch_size = num_matrices ÷ n
+    R = real(T)
+    lda = max(1, stride(A, 2))
+    W = CuVector{R}(undef, n * batch_size)
+    params = CuSolverParameters()
+    dh = dense_handle()
+    resize!(dh.info, batch_size)
+
+    function bufferSize()
+        out_cpu = Ref{Csize_t}(0)
+        out_gpu = Ref{Csize_t}(0)
+        cusolverDnXsyevBatched_bufferSize(dh, params, jobz, uplo, n,
+                                          T, A, lda, R, W, T, out_gpu, out_cpu, batch_size)
+        out_gpu[], out_cpu[]
+    end
+    with_workspaces(dh.workspace_gpu, dh.workspace_cpu, bufferSize()...) do buffer_gpu, buffer_cpu
+        cusolverDnXsyevBatched(dh, params, jobz, uplo, n, T, A,
+                               lda, R, W, T, buffer_gpu, sizeof(buffer_gpu),
+                               buffer_cpu, sizeof(buffer_cpu), dh.info, batch_size)
+    end
+
+    info = @allowscalar collect(dh.info)
+    for i = 1:batch_size
+        chkargsok(info[i] |> BlasInt)
+    end
+
+    if jobz == 'N'
+        return W
+    elseif jobz == 'V'
+        return W, A
+    end
+end
+
 # LAPACK
 for elty in (:Float32, :Float64, :ComplexF32, :ComplexF64)
     @eval begin
diff --git a/test/libraries/cusolver/dense_generic.jl b/test/libraries/cusolver/dense_generic.jl
index ffe0ac549c..77a97c4155 100644
--- a/test/libraries/cusolver/dense_generic.jl
+++ b/test/libraries/cusolver/dense_generic.jl
@@ -31,6 +31,36 @@ p = 5
                 end
             end
         end
+
+        @testset "syevBatched!" begin
+            batch_size = 5
+            for uplo in ('L', 'U')
+                (uplo == 'L') && (elty == ComplexF32) && continue
+
+                A = rand(elty, n, n * batch_size)
+                B = rand(elty, n, n * batch_size)
+                for i = 1:batch_size
+                    S = rand(elty,n,n)
+                    S = S * S' + I
+                    B[:,(i-1)*n+1:i*n] .= S
+                    S = uplo == 'L' ? tril(S) : triu(S)
+                    A[:,(i-1)*n+1:i*n] .= S
+                end
+                d_A = CuMatrix(A)
+                d_W, d_V = CUSOLVER.XsyevBatched!('V', uplo, d_A)
+                W = collect(d_W)
+                V = collect(d_V)
+                for i = 1:batch_size
+                    Bᵢ = B[:,(i-1)*n+1:i*n]
+                    Wᵢ = Diagonal(W[(i-1)*n+1:i*n])
+                    Vᵢ = V[:,(i-1)*n+1:i*n]
+                    @test Bᵢ * Vᵢ ≈ Vᵢ * Diagonal(Wᵢ)
+                end
+
+                d_A = CuMatrix(A)
+                d_W = CUSOLVER.XsyevBatched!('N', uplo, d_A)
+            end
+        end
     end
 
     if CUSOLVER.version() >= v"11.6.0"