diff --git a/Project.toml b/Project.toml
index ff6661eb..ae05f972 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "StaticArrays"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "1.9.4"
+version = "1.9.5"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
diff --git a/src/StaticArrays.jl b/src/StaticArrays.jl
index 0e4b1afe..c4a0437e 100644
--- a/src/StaticArrays.jl
+++ b/src/StaticArrays.jl
@@ -133,6 +133,10 @@ include("flatten.jl")
 include("io.jl")
 include("pinv.jl")
 
+@static if VERSION >= v"1.7"
+    include("blas.jl")
+end
+
 @static if !isdefined(Base, :get_extension) # VERSION < v"1.9-"
     include("../ext/StaticArraysStatisticsExt.jl")
 end
diff --git a/src/blas.jl b/src/blas.jl
new file mode 100644
index 00000000..e019c2a9
--- /dev/null
+++ b/src/blas.jl
@@ -0,0 +1,141 @@
+# This file contains funtions that uses a direct interface to BLAS library. We use this
+# approach to reduce allocations.
+
+import LinearAlgebra: BLAS, LAPACK, libblastrampoline
+
+# == Singular Value Decomposition ==========================================================
+
+# Implement direct call to BLAS functions that computes the SVD values for `SMatrix` and
+# `MMatrix` reducing allocations. In this case, we use `MMatrix` to call the library and
+# convert the result back to the input type. Since the former does not exit this scope, we
+# can reduce allocations.
+#
+# We are implementing here the following functions:
+#
+#   svdvals(A::SMatrix{M, N, Float64}) where {M, N}
+#   svdvals(A::SMatrix{M, N, Float32}) where {M, N}
+#   svdvals(A::MMatrix{M, N, Float64}) where {M, N}
+#   svdvals(A::MMatrix{M, N, Float32}) where {M, N}
+#
+for (gesdd, elty) in ((:dgesdd_, :Float64), (:sgesdd_, :Float32)),
+    (mtype, vtype) in ((SMatrix, SVector), (MMatrix, MVector))
+
+    blas_func = @eval BLAS.@blasfunc($gesdd)
+
+    @eval begin
+        function svdvals(A::$mtype{M, N, $elty}) where {M, N}
+            K = min(M, N)
+
+            # Convert the input to a `MMatrix` and allocate the required arrays.
+            Am    = MMatrix{M, N, $elty}(A)
+            Sm    = MVector{K, $elty}(undef)
+
+            # We compute the `lwork` (size of the work array) by obtaining the maximum value
+            # from the possibilities shown in:
+            #   https://docs.oracle.com/cd/E19422-01/819-3691/dgesdd.html
+            lwork = max(8N, 3N + max(M, 7N), 8M, 3M + max(N, 7M))
+            work  = MVector{lwork, $elty}(undef)
+            iwork = MVector{8min(M, N), BLAS.BlasInt}(undef)
+            info  = Ref(1)
+
+            @ccall libblastrampoline.$blas_func(
+                'N'::Ref{UInt8},
+                M::Ref{BLAS.BlasInt},
+                N::Ref{BLAS.BlasInt},
+                Am::Ptr{$elty},
+                M::Ref{BLAS.BlasInt},
+                Sm::Ptr{$elty},
+                C_NULL::Ptr{C_NULL},
+                M::Ref{BLAS.BlasInt},
+                C_NULL::Ptr{C_NULL},
+                K::Ref{BLAS.BlasInt},
+                work::Ptr{$elty},
+                lwork::Ref{BLAS.BlasInt},
+                iwork::Ptr{BLAS.BlasInt},
+                info::Ptr{BLAS.BlasInt},
+                1::Clong
+            )::Cvoid
+
+            # Check if the return result of the function.
+            LAPACK.chklapackerror(info.x)
+
+            # Convert the vector to static arrays and return.
+            S = $vtype{K, $elty}(Sm)
+
+            return S
+        end
+    end
+end
+
+# For matrices with interger numbers, we should promote them to float and call `svdvals`.
+@inline svdvals(A::StaticMatrix{<: Any, <: Any, <: Integer}) = svdvals(float(A))
+
+# Implement direct call to BLAS functions that computes the SVD for `SMatrix` and `MMatrix`
+# reducing allocations. In this case, we use `MMatrix` to call the library and convert the
+# result back to the input type. Since the former does not exit this scope, we can reduce
+# allocations.
+#
+# We are implementing here the following functions:
+#
+#   _svd(A::SMatrix{M, N, Float64}, full::Val{false}) where {M, N}
+#   _svd(A::SMatrix{M, N, Float64}, full::Val{true})  where {M, N}
+#   _svd(A::SMatrix{M, N, Float32}, full::Val{false}) where {M, N}
+#   _svd(A::SMatrix{M, N, Float32}, full::Val{true})  where {M, N}
+#   _svd(A::MMatrix{M, N, Float64}, full::Val{false}) where {M, N}
+#   _svd(A::MMatrix{M, N, Float64}, full::Val{true})  where {M, N}
+#   _svd(A::MMatrix{M, N, Float32}, full::Val{false}) where {M, N}
+#   _svd(A::MMatrix{M, N, Float32}, full::Val{true})  where {M, N}
+#
+for (gesvd, elty) in ((:dgesvd_, :Float64), (:sgesvd_, :Float32)),
+    full in (false, true),
+    (mtype, vtype) in ((SMatrix, SVector), (MMatrix, MVector))
+
+    blas_func = @eval BLAS.@blasfunc($gesvd)
+
+    @eval begin
+        function _svd(A::$mtype{M, N, $elty}, full::Val{$full}) where {M, N}
+            K = min(M, N)
+
+            # Convert the input to a `MMatrix` and allocate the required arrays.
+            Am    = MMatrix{M, N, $elty}(A)
+            Um    = MMatrix{M, $(full ? :M : :K), $elty}(undef)
+            Sm    = MVector{K, $elty}(undef)
+            Vtm   = MMatrix{$(full ? :N : :K), N, $elty}(undef)
+            lwork = max(3min(M, N) + max(M, N), 5min(M, N))
+            work  = MVector{lwork, $elty}(undef)
+            info  = Ref(1)
+
+            @ccall libblastrampoline.$blas_func(
+                $(full ? 'A' : 'S')::Ref{UInt8},
+                $(full ? 'A' : 'S')::Ref{UInt8},
+                M::Ref{BLAS.BlasInt},
+                N::Ref{BLAS.BlasInt},
+                Am::Ptr{$elty},
+                M::Ref{BLAS.BlasInt},
+                Sm::Ptr{$elty},
+                Um::Ptr{$elty},
+                M::Ref{BLAS.BlasInt},
+                Vtm::Ptr{$elty},
+                $(full ? :N : :K)::Ref{BLAS.BlasInt},
+                work::Ptr{$elty},
+                lwork::Ref{BLAS.BlasInt},
+                info::Ptr{BLAS.BlasInt},
+                1::Clong,
+                1::Clong
+            )::Cvoid
+
+            # Check if the return result of the function.
+            LAPACK.chklapackerror(info.x)
+
+            # Convert the matrices to the correct type and return.
+            U  = $mtype{M, $(full ? :M : :K), $elty}(Um)
+            S  = $vtype{K, $elty}(Sm)
+            Vt = $mtype{$(full ? :N : :K), N, $elty}(Vtm)
+
+            return SVD(U, S, Vt)
+        end
+    end
+end
+
+# For matrices with interger numbers, we should promote them to float and call `svd`.
+@inline svd(A::StaticMatrix{<: Any, <: Any, <: Integer}) = svd(float(A))
diff --git a/src/svd.jl b/src/svd.jl
index d2af6e90..b8d1e0e2 100644
--- a/src/svd.jl
+++ b/src/svd.jl
@@ -73,3 +73,4 @@ function diagmult(sd, sB, d, B)
     ind = SOneTo(sd[1])
     return isa(B, AbstractVector) ? Diagonal(d)*B[ind] : Diagonal(d)*B[ind,:]
 end
+
diff --git a/test/svd.jl b/test/svd.jl
index 0df776f0..d377dc3e 100644
--- a/test/svd.jl
+++ b/test/svd.jl
@@ -2,8 +2,10 @@ using StaticArrays, Test, LinearAlgebra
 
 @testset "SVD factorization" begin
     m3 = @SMatrix Float64[3 9 4; 6 6 2; 3 7 9]
+    m3_f32 = @SMatrix Float32[3 9 4; 6 6 2; 3 7 9]
     m3c = ComplexF64.(m3)
     m23 = @SMatrix Float64[3 9 4; 6 6 2]
+    m23_f32 = @SMatrix Float32[3 9 4; 6 6 2]
     m_sing = @SMatrix [2.0 3.0 5.0; 4.0 6.0 10.0; 1.0 1.0 1.0]
     m_sing2 = @SMatrix [1 1; 1 0; 0 1]
     v = @SVector [1, 2, 3]
@@ -18,6 +20,7 @@ using StaticArrays, Test, LinearAlgebra
         @testinf svdvals((@SMatrix [2 -2; 1 1]) / sqrt(2)) ≊ [2, 1]
 
         @testinf svdvals(m3) ≊ svdvals(Matrix(m3))
+        @testinf svdvals(m3_f32) ≊ svdvals(Matrix(m3_f32))
         @testinf svdvals(m3c) isa SVector{3,Float64}
 
         @testinf svd(m3).U::StaticMatrix ≊ svd(Matrix(m3)).U
@@ -25,9 +28,14 @@ using StaticArrays, Test, LinearAlgebra
         @testinf svd(m3).V::StaticMatrix ≊ svd(Matrix(m3)).V
         @testinf svd(m3).Vt::StaticMatrix ≊ svd(Matrix(m3)).Vt
 
-        @testinf svd(@SMatrix [2 0; 0 0]).U === one(SMatrix{2,2})
-        @testinf svd(@SMatrix [2 0; 0 0]).S === SVector(2.0, 0.0)
-        @testinf svd(@SMatrix [2 0; 0 0]).Vt === one(SMatrix{2,2})
+        @test svd(m3_f32).U::StaticMatrix ≈ svd(Matrix(m3_f32)).U atol = 5e-7
+        @test svd(m3_f32).S::StaticVector ≈ svd(Matrix(m3_f32)).S atol = 5e-7
+        @test svd(m3_f32).V::StaticMatrix ≈ svd(Matrix(m3_f32)).V atol = 5e-7
+        @test svd(m3_f32).Vt::StaticMatrix ≈ svd(Matrix(m3_f32)).Vt atol = 5e-7
+
+        @testinf svd(@SMatrix [2 0; 0 0]).U ≊ one(SMatrix{2,2})
+        @testinf svd(@SMatrix [2 0; 0 0]).S ≊ SVector(2.0, 0.0)
+        @testinf svd(@SMatrix [2 0; 0 0]).Vt ≊ one(SMatrix{2,2})
 
         @testinf svd((@SMatrix [2 -2; 1 1]) / sqrt(2)).U ≊ [-1 0; 0 1]
         @testinf svd((@SMatrix [2 -2; 1 1]) / sqrt(2)).S ≊ [2, 1]
@@ -41,6 +49,16 @@ using StaticArrays, Test, LinearAlgebra
         @testinf svd(m23').S  ≊ svd(Matrix(m23')).S
         @testinf svd(m23').Vt ≊ svd(Matrix(m23')).Vt
 
+        @test svd(m23_f32).U::StaticMatrix ≈ svd(Matrix(m23_f32)).U atol = 5e-7
+        @test svd(m23_f32).S::StaticVector ≈ svd(Matrix(m23_f32)).S atol = 5e-7
+        @test svd(m23_f32).V::StaticMatrix ≈ svd(Matrix(m23_f32)).V atol = 5e-7
+        @test svd(m23_f32).Vt::StaticMatrix ≈ svd(Matrix(m23_f32)).Vt atol = 5e-7
+
+        @test svd(m23_f32').U::StaticMatrix ≈ svd(Matrix(m23_f32')).U atol = 5e-7
+        @test svd(m23_f32').S::StaticVector ≈ svd(Matrix(m23_f32')).S atol = 5e-7
+        @test svd(m23_f32').V::StaticMatrix ≈ svd(Matrix(m23_f32')).V atol = 5e-7
+        @test svd(m23_f32').Vt::StaticMatrix ≈ svd(Matrix(m23_f32')).Vt atol = 5e-7
+
         @testinf svd(m23, full=true).U::StaticMatrix  ≊ svd(Matrix(m23), full=true).U
         @testinf svd(m23, full=true).S::StaticVector  ≊ svd(Matrix(m23), full=true).S
         @testinf svd(m23, full=true).Vt::StaticMatrix ≊ svd(Matrix(m23), full=true).Vt