diff --git a/test/Project.toml b/test/Project.toml
index a05d3bd..a5a5fb5 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -22,6 +22,7 @@ NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
+Reactant = "3c362404-f566-11ee-1572-e11a4b42c853"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
@@ -51,6 +52,7 @@ NNlib = "0.9.21"
 Pkg = "1.10"
 Random = "1.10"
 ReTestItems = "1.24.0"
+Reactant = "0.2.5"
 Reexport = "1.2.2"
 StableRNGs = "1.0.2"
 Test = "1.10"
diff --git a/test/layer_tests.jl b/test/layer_tests.jl
index 3477ba0..52c1c05 100644
--- a/test/layer_tests.jl
+++ b/test/layer_tests.jl
@@ -16,14 +16,33 @@
 
                 model = Layers.MLP(2, (4, 4, 2), act; norm_layer=norm)
                 ps, st = Lux.setup(StableRNG(0), model) |> dev
+                st_test = Lux.testmode(st)
 
                 x = randn(Float32, 2, 2) |> aType
 
-                @jet model(x, ps, st)
+                @jet model(x, ps, st_test)
 
                 __f = (x, ps) -> sum(abs2, first(model(x, ps, st)))
                 @test_gradients(__f, x, ps; atol=1e-3, rtol=1e-3,
                     soft_fail=[AutoFiniteDiff()])
+
+                if test_reactant(mode)
+                    set_reactant_backend!(mode)
+                    rdev = reactant_device()
+
+                    ps_ra = rdev(ps)
+                    st_ra = rdev(st_test)
+                    x_ra = rdev(x)
+
+                    model_compiled = Reactant.compile(model, (x_ra, ps_ra, st_ra))
+                    if nType === GroupNorm
+                        @test first(model_compiled(x_ra, ps_ra, st_ra)) ≈
+                              zeros(Float32, 2, 2)
+                    else
+                        @test first(model_compiled(x_ra, ps_ra, st_ra)) ≈
+                              Array(first(model(x, ps, st_test)))
+                    end
+                end
             end
         end
     end
@@ -217,6 +236,10 @@ end
 
         __f = x -> sum(first(layer(x, ps, st)))
         @test_gradients(__f, x; atol=1.0f-3, rtol=1.0f-3, enzyme_set_runtime_activity=true)
+
+        # TODO: Reactant testing
+        # We need to solve https://github.com/EnzymeAD/Reactant.jl/issues/242 and
+        # https://github.com/EnzymeAD/Reactant.jl/issues/243 first
     end
 end
 
diff --git a/test/shared_testsetup.jl b/test/shared_testsetup.jl
index dc8e429..b5bb538 100644
--- a/test/shared_testsetup.jl
+++ b/test/shared_testsetup.jl
@@ -19,6 +19,24 @@ if BACKEND_GROUP == "all" || BACKEND_GROUP == "amdgpu"
     using AMDGPU
 end
 
+@static if !Sys.iswindows()
+    @reexport using Reactant
+    test_reactant(mode) = mode != "amdgpu"
+    function set_reactant_backend!(mode)
+        if mode == "cuda"
+            Reactant.set_default_backend("gpu")
+        elseif mode == "cpu"
+            Reactant.set_default_backend("cpu")
+        end
+    end
+else
+    test_reactant(::Any) = true
+    set_reactant_backend!(::Any) = nothing
+    macro compile(expr)
+        return :()
+    end
+end
+
 cpu_testing() = BACKEND_GROUP == "all" || BACKEND_GROUP == "cpu"
 function cuda_testing()
     return (BACKEND_GROUP == "all" || BACKEND_GROUP == "cuda") &&
@@ -38,5 +56,7 @@ const MODES = begin
 end
 
 export MODES, BACKEND_GROUP
+export test_reactant, set_reactant_backend!
+export @compile
 
 end
diff --git a/test/vision_tests.jl b/test/vision_tests.jl
index 449e90d..12903b1 100644
--- a/test/vision_tests.jl
+++ b/test/vision_tests.jl
@@ -2,6 +2,10 @@
 
 using Lux, Downloads, JLD2
 
+@static if !Sys.iswindows()
+    using Reactant
+end
+
 function normalize_imagenet(data)
     cmean = reshape(Float32[0.485, 0.456, 0.406], (1, 1, 3, 1))
     cstd = reshape(Float32[0.229, 0.224, 0.225], (1, 1, 3, 1))
@@ -23,7 +27,12 @@ function imagenet_acctest(model, ps, st, dev; size=224)
     TEST_X = size == 224 ? MONARCH_224 :
              (size == 256 ? MONARCH_256 : error("size must be 224 or 256"))
     x = TEST_X |> dev
-    ypred = first(model(x, ps, st)) |> collect |> vec
+
+    if dev isa MLDataDevices.ReactantDevice
+        model = Reactant.compile(model, (x, ps, st))
+    end
+
+    ypred = first(model(x, ps, st)) |> cpu_device() |> collect |> vec
     top5 = TEST_LBLS[sortperm(ypred; rev=true)]
     return "monarch" in top5
 end
@@ -48,6 +57,23 @@ end
             end
 
             GC.gc(true)
+
+            if test_reactant(mode)
+                set_reactant_backend!(mode)
+                rdev = reactant_device()
+
+                ps_ra = rdev(ps)
+                st_ra = rdev(st)
+                img_ra = rdev(img)
+
+                model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+                @test first(model_compiled(
+                    img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+
+                if pretrained
+                    @test imagenet_acctest(model, ps_ra, st_ra, rdev)
+                end
+            end
         end
     end
 end
@@ -63,6 +89,19 @@ end
         @test size(first(model(img, ps, st))) == (1000, 2)
 
         GC.gc(true)
+
+        if test_reactant(mode)
+            set_reactant_backend!(mode)
+            rdev = reactant_device()
+
+            ps_ra = rdev(ps)
+            st_ra = rdev(st)
+            img_ra = rdev(img)
+
+            model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+            @test first(model_compiled(
+                img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+        end
     end
 end
 
@@ -77,6 +116,19 @@ end
         @test size(first(model(img, ps, st))) == (1000, 2)
 
         GC.gc(true)
+
+        if test_reactant(mode)
+            set_reactant_backend!(mode)
+            rdev = reactant_device()
+
+            ps_ra = rdev(ps)
+            st_ra = rdev(st)
+            img_ra = rdev(img)
+
+            model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+            @test first(model_compiled(
+                img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+        end
     end
 end
 
@@ -91,6 +143,19 @@ end
         @test size(first(model(img, ps, st))) == (1000, 2)
 
         GC.gc(true)
+
+        if test_reactant(mode)
+            set_reactant_backend!(mode)
+            rdev = reactant_device()
+
+            ps_ra = rdev(ps)
+            st_ra = rdev(st)
+            img_ra = rdev(img)
+
+            model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+            @test first(model_compiled(
+                img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+        end
     end
 end
 
@@ -110,6 +175,23 @@ end
             end
 
             GC.gc(true)
+
+            if test_reactant(mode)
+                set_reactant_backend!(mode)
+                rdev = reactant_device()
+
+                ps_ra = rdev(ps)
+                st_ra = rdev(st)
+                img_ra = rdev(img)
+
+                model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+                @test first(model_compiled(
+                    img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+
+                if pretrained
+                    @test imagenet_acctest(model, ps_ra, st_ra, rdev)
+                end
+            end
         end
     end
 end
@@ -134,6 +216,23 @@ end
                 end
 
                 GC.gc(true)
+
+                if test_reactant(mode)
+                    set_reactant_backend!(mode)
+                    rdev = reactant_device()
+
+                    ps_ra = rdev(ps)
+                    st_ra = rdev(st)
+                    img_ra = rdev(img)
+
+                    model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+                    @test first(model_compiled(
+                        img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+
+                    if pretrained
+                        @test imagenet_acctest(model, ps_ra, st_ra, rdev)
+                    end
+                end
             end
         end
     end
@@ -157,6 +256,23 @@ end
             end
 
             GC.gc(true)
+
+            if test_reactant(mode)
+                set_reactant_backend!(mode)
+                rdev = reactant_device()
+
+                ps_ra = rdev(ps)
+                st_ra = rdev(st)
+                img_ra = rdev(img)
+
+                model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+                @test first(model_compiled(
+                    img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+
+                if pretrained
+                    @test imagenet_acctest(model, ps_ra, st_ra, rdev)
+                end
+            end
         end
     end
 end
@@ -177,6 +293,23 @@ end
             end
 
             GC.gc(true)
+
+            if test_reactant(mode)
+                set_reactant_backend!(mode)
+                rdev = reactant_device()
+
+                ps_ra = rdev(ps)
+                st_ra = rdev(st)
+                img_ra = rdev(img)
+
+                model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+                @test first(model_compiled(
+                    img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+
+                if pretrained
+                    @test imagenet_acctest(model, ps_ra, st_ra, rdev)
+                end
+            end
         end
     end
 end
@@ -197,6 +330,23 @@ end
             end
 
             GC.gc(true)
+
+            if test_reactant(mode)
+                set_reactant_backend!(mode)
+                rdev = reactant_device()
+
+                ps_ra = rdev(ps)
+                st_ra = rdev(st)
+                img_ra = rdev(img)
+
+                model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+                @test first(model_compiled(
+                    img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+
+                if pretrained
+                    @test imagenet_acctest(model, ps_ra, st_ra, rdev)
+                end
+            end
         end
     end
 end
@@ -221,5 +371,18 @@ end
         @test size(first(model(img, ps, st))) == (1000, 2)
 
         GC.gc(true)
+
+        if test_reactant(mode)
+            set_reactant_backend!(mode)
+            rdev = reactant_device()
+
+            ps_ra = rdev(ps)
+            st_ra = rdev(st)
+            img_ra = rdev(img)
+
+            model_compiled = Reactant.compile(model, (img_ra, ps_ra, st_ra))
+            @test first(model_compiled(
+                img_ra, ps_ra, st_ra))≈Array(first(model(img, ps, st))) atol=1e-3 rtol=1e-3
+        end
     end
 end