diff --git a/lib/cusparse/generic.jl b/lib/cusparse/generic.jl
index 0aed52115d..e944645760 100644
--- a/lib/cusparse/generic.jl
+++ b/lib/cusparse/generic.jl
@@ -1,7 +1,7 @@
 # generic APIs
 
 export gather!, scatter!, axpby!, rot!
-export vv!, sv!, sm!, gemm, gemm!, sddmm!
+export vv!, sv!, sm!, gemv, gemm, gemm!, sddmm!
 export bmm!
 
 ## API functions
@@ -574,6 +574,43 @@ function gemm(transa::SparseChar, transb::SparseChar, alpha::Number, A::CuSparse
     return C
 end
 
+"""
+    y = gemv(transa, alpha, A, x, index, [algo])
+
+Perform a product between a `CuSparseMatrix` and a `CuSparseVector`, returning a `CuSparseVector`.
+This function should only be used for highly sparse matrices and vectors, as the result is expected
+to have many non-zeros in practice.
+For this reason, high-level functions like `mul!` and `*` internally convert the sparse vector into a
+dense vector to use a more efficient CUSPARSE routine.
+
+Supported formats for the sparse matrix are `CuSparseMatrixCSC` and `CuSparseMatrixCSR`.
+"""
+function gemv end
+
+function gemv(transa::SparseChar, alpha::Number, A::CuSparseMatrixCSC{T},
+              x::CuSparseVector{T}, index::SparseChar, algo::cusparseSpGEMMAlg_t=CUSPARSE_SPGEMM_DEFAULT) where {T}
+    m, n = size(A)
+    p = length(x)
+    p == n || throw(DimensionMismatch("dimensions must match: x has length $p, A has length $m × $n"))
+    # we model x as a CuSparseMatrixCSC with one column.
+    B = CuSparseMatrixCSC(x)
+    C = gemm(transa, 'N', alpha, A, B, index, algo)
+    y = CuSparseVector(C)
+    return y
+end
+
+function gemv(transa::SparseChar, alpha::Number, A::CuSparseMatrixCSR{T},
+              x::CuSparseVector{T}, index::SparseChar, algo::cusparseSpGEMMAlg_t=CUSPARSE_SPGEMM_DEFAULT) where {T}
+    m, n = size(A)
+    p = length(x)
+    p == n || throw(DimensionMismatch("dimensions must match: x has length $p, A has length $m × $n"))
+    # we model x as a CuSparseMatrixCSR with one column.
+    B = CuSparseMatrixCSR(x)
+    C = gemm(transa, 'N', alpha, A, B, index, algo)
+    y = CuSparseVector(C)
+    return y
+end
+
 for SparseMatrixType in (:CuSparseMatrixCSC, :CuSparseMatrixCSR)
     @eval begin
         function gemm(transa::SparseChar, transb::SparseChar, alpha::Number, A::$SparseMatrixType{T}, B::$SparseMatrixType{T},
diff --git a/lib/cusparse/interfaces.jl b/lib/cusparse/interfaces.jl
index 27b10b8ce4..f6a7437162 100644
--- a/lib/cusparse/interfaces.jl
+++ b/lib/cusparse/interfaces.jl
@@ -73,6 +73,7 @@ function LinearAlgebra.generic_matvecmul!(C::CuVector{T}, tA::AbstractChar, A::C
     tA = tA in ('S', 's', 'H', 'h') ? 'N' : tA
     mv_wrapper(tA, alpha, A, B, beta, C)
 end
+
 function LinearAlgebra.generic_matvecmul!(C::CuVector{T}, tA::AbstractChar, A::CuSparseMatrix{T}, B::CuSparseVector{T}, alpha::Number, beta::Number) where {T <: Union{Float16, ComplexF16, BlasFloat}}
     tA = tA in ('S', 's', 'H', 'h') ? 'N' : tA
     mv_wrapper(tA, alpha, A, CuVector{T}(B), beta, C)
diff --git a/test/libraries/cusparse/generic.jl b/test/libraries/cusparse/generic.jl
index 4be9183839..1c6c74c05e 100644
--- a/test/libraries/cusparse/generic.jl
+++ b/test/libraries/cusparse/generic.jl
@@ -358,6 +358,19 @@ for SparseMatrixType in keys(SPGEMM_ALGOS)
             end
         end
     end
+
+    @testset "gemv $T" for T in [Float32, Float64, ComplexF32, ComplexF64]
+        for (transa, opa) in [('N', identity)]
+            A = sprand(T,25,10,0.2)
+            b = sprand(T,10,0.3)
+            dA = SparseMatrixType(A)
+            db = CuSparseVector(b)
+            alpha = rand(T)
+            y = alpha * opa(A) * b
+            dy = gemv(transa, alpha, dA, db, 'O')
+            @test collect(dy) ≈ y
+        end
+    end
 end
 
 if CUSPARSE.version() >= v"11.4.1"