diff --git a/src/precompile.jl b/src/precompile.jl
index 1db5f2ab6..74c096bb1 100644
--- a/src/precompile.jl
+++ b/src/precompile.jl
@@ -1,64 +1,13 @@
+# Putting some things in `@setup_workload` instead of `@compile_workload` can reduce the
+# size of the precompile file and potentially make loading faster.
+@setup_workload begin
+
 sys = [ 
     tf(1.90, [1800, 1]) tf(1.90, [1800, 1]);
     tf(-0.74,[800, 1])  tf(0.74, [800, 1]) 
 ]
 Ts = 400.0
-model = setop!(LinModel(sys, Ts), uop=[10, 10], yop=[50, 30])
-y = model()
-
-mpc_im = setconstraint!(LinMPC(InternalModel(model)), ymin=[45, -Inf])
-initstate!(mpc_im, model.uop, y)
-preparestate!(mpc_im, [55, 30])
-mpc_im.estim()
-u = mpc_im([55, 30])
-sim!(mpc_im, 2)
-
-transcription = MultipleShooting()
-mpc_kf = setconstraint!(LinMPC(KalmanFilter(model); transcription), ymin=[45, -Inf])
-initstate!(mpc_kf, model.uop, model())
-preparestate!(mpc_kf, [55, 30])
-mpc_kf.estim()
-u = mpc_kf([55, 30])
-sim!(mpc_kf, 2, [55, 30])
-
-mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ymin=[45, -Inf])
-initstate!(mpc_skf, model.uop, model())
-preparestate!(mpc_skf, [55, 30])
-mpc_skf.estim()
-u = mpc_skf([55, 30])
-sim!(mpc_skf, 2, [55, 30])
-
-mpc_mhe = setconstraint!(LinMPC(MovingHorizonEstimator(model, He=2)), ymin=[45, -Inf])
-setconstraint!(mpc_mhe.estim, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
-initstate!(mpc_mhe, model.uop, model())
-preparestate!(mpc_mhe, [55, 30])
-mpc_mhe.estim()
-u = mpc_mhe([55, 30])
-sim!(mpc_mhe, 2, [55, 30])
-
-mpc_man = setconstraint!(LinMPC(ManualEstimator(model)), ymin=[45, -Inf])
-initstate!(mpc_man, model.uop, model())
-setstate!(mpc_man, ones(4))
-u = mpc_man([55, 30])
-
-nmpc_skf = setconstraint!(NonLinMPC(SteadyKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
-initstate!(nmpc_skf, model.uop, model())
-preparestate!(nmpc_skf, [55, 30])
-nmpc_skf.estim()
-u = nmpc_skf([55, 30])
-sim!(nmpc_skf, 2, [55, 30])
-
-res = sim!(model, 2)
-
-res_man = SimResult(model, res.U_data, res.Y_data; X_data=res.X_data)
-
-exmpc = ExplicitMPC(model)
-initstate!(exmpc, model.uop, model())
-preparestate!(exmpc, [55, 30])
-exmpc.estim()
-u = exmpc([55, 30])
-sim!(exmpc, 2, [55, 30])
-
+sys2 = minreal(ss(sys))
 function f!(xnext, x, u, _ , p)
     A, B, _ = p
     mul!(xnext, A , x)
@@ -70,38 +19,7 @@ function h!(y, x, _ , p)
     mul!(y, C, x)
     return nothing
 end
-
-sys2 = minreal(ss(sys))
-nlmodel = setop!(
-    NonLinModel(f!, h!, Ts, 2, 2, 2, solver=RungeKutta(4), p=(sys2.A, sys2.B, sys2.C)), 
-    uop=[10, 10], yop=[50, 30]
-)
-y = nlmodel()
-
-transcription = MultipleShooting(f_threads=true)
-nmpc_ukf = setconstraint!(NonLinMPC(
-    UnscentedKalmanFilter(nlmodel); Hp=10, transcription, Cwt=1e3), ymin=[45, -Inf]
-)
-initstate!(nmpc_ukf, nlmodel.uop, y)
-preparestate!(nmpc_ukf, [55, 30])
-u = nmpc_ukf([55, 30])
-sim!(nmpc_ukf, 2, [55, 30])
-
-nmpc_ekf = setconstraint!(NonLinMPC(ExtendedKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
-initstate!(nmpc_ekf, model.uop, model())
-preparestate!(nmpc_ekf, [55, 30])
-u = nmpc_ekf([55, 30])
-sim!(nmpc_ekf, 2, [55, 30])
-
-transcription = TrapezoidalCollocation()
-nmpc_mhe = setconstraint!(NonLinMPC(
-    MovingHorizonEstimator(nlmodel, He=2); transcription, Hp=10, Cwt=Inf), ymin=[45, -Inf]
-)
-setconstraint!(nmpc_mhe.estim, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
-initstate!(nmpc_mhe, nlmodel.uop, y)
-preparestate!(nmpc_mhe, [55, 30])
-u = nmpc_mhe([55, 30])
-sim!(nmpc_mhe, 2, [55, 30])
+p = (sys2.A, sys2.B, sys2.C)
 
 function JE( _ , Ŷe, _ , R̂y)
     Ŷ = @views Ŷe[3:end]
@@ -109,12 +27,98 @@ function JE( _ , Ŷe, _ , R̂y)
     return dot(Ȳ, Ȳ)
 end
 R̂y = repeat([55; 30], 10)
-empc = setconstraint!(
-    NonLinMPC(nlmodel, Mwt=[0, 0], Hp=10, Cwt=Inf, Ewt=1, JE=JE, p=R̂y), ymin=[45, -Inf]
-)
-preparestate!(empc, [55, 30])
-u = empc()
-sim!(empc, 2)
-
-linearizemodel = linearize(nlmodel)
-setmodel!(mpc_kf, linearizemodel)
\ No newline at end of file
+
+# all calls in this block will be precompiled, regardless of whether
+# they belong to your package or not (on Julia 1.8 and higher)
+@compile_workload begin
+
+    model = setop!(LinModel(sys, Ts), uop=[10, 10], yop=[50, 30])
+    y = model()
+
+    mpc_im = setconstraint!(LinMPC(InternalModel(model)), ymin=[45, -Inf])
+    initstate!(mpc_im, model.uop, y)
+    preparestate!(mpc_im, [55, 30])
+    mpc_im.estim()
+    u = mpc_im([55, 30])
+    sim!(mpc_im, 2)
+
+    mpc_skf = setconstraint!(LinMPC(SteadyKalmanFilter(model)), ymin=[45, -Inf])
+    initstate!(mpc_skf, model.uop, model())
+    preparestate!(mpc_skf, [55, 30])
+    mpc_skf.estim()
+    u = mpc_skf([55, 30])
+    sim!(mpc_skf, 2, [55, 30])
+
+    transcription = MultipleShooting()
+    mpc_kf = setconstraint!(LinMPC(KalmanFilter(model); transcription), ymin=[45, -Inf])
+    initstate!(mpc_kf, model.uop, model())
+    preparestate!(mpc_kf, [55, 30])
+    mpc_kf.estim()
+    u = mpc_kf([55, 30])
+    sim!(mpc_kf, 2, [55, 30])
+
+    mhe = MovingHorizonEstimator(model, He=2, direct=true)
+    mhe = setconstraint!(mhe, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
+    initstate!(mhe, model.uop, model())
+    preparestate!(mhe, [55, 30])
+    mhe()
+
+    mpc_man = setconstraint!(LinMPC(ManualEstimator(model)), ymin=[45, -Inf])
+    initstate!(mpc_man, model.uop, model())
+    setstate!(mpc_man, ones(4))
+    mpc_man([55, 30])
+
+    nmpc_skf = setconstraint!(NonLinMPC(SteadyKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
+    initstate!(nmpc_skf, model.uop, model())
+    preparestate!(nmpc_skf, [55, 30])
+    nmpc_skf.estim()
+    nmpc_skf([55, 30])
+
+    res = sim!(model, 2)
+    res_man = SimResult(model, res.U_data, res.Y_data; X_data=res.X_data)
+
+    exmpc = ExplicitMPC(model)
+    initstate!(exmpc, model.uop, model())
+    preparestate!(exmpc, [55, 30])
+    exmpc.estim()
+    exmpc([55, 30])
+
+    nlmodel = NonLinModel(f!, h!, Ts, 2, 2, 2; solver=RungeKutta(4), p)
+    nlmodel = setop!(nlmodel, uop=[10, 10], yop=[50, 30])
+    y = nlmodel()
+
+    transcription = MultipleShooting(f_threads=true)
+    nmpc_ukf = setconstraint!(NonLinMPC(
+        UnscentedKalmanFilter(nlmodel); Hp=10, transcription, Cwt=1e3), ymin=[45, -Inf]
+    )
+    initstate!(nmpc_ukf, nlmodel.uop, y)
+    preparestate!(nmpc_ukf, [55, 30])
+    nmpc_ukf([55, 30])
+    sim!(nmpc_ukf, 2, [55, 30])
+
+    nmpc_ekf = setconstraint!(NonLinMPC(ExtendedKalmanFilter(model), Cwt=Inf), ymin=[45, -Inf])
+    initstate!(nmpc_ekf, model.uop, model())
+    preparestate!(nmpc_ekf, [55, 30])
+    nmpc_ekf([55, 30])
+
+    transcription = TrapezoidalCollocation()
+    nmpc_mhe = setconstraint!(NonLinMPC(
+        MovingHorizonEstimator(nlmodel, He=2); transcription, Hp=10, Cwt=Inf), ymin=[45, -Inf]
+    )
+    setconstraint!(nmpc_mhe.estim, x̂min=[-50,-50,-50,-50], x̂max=[50,50,50,50])
+    initstate!(nmpc_mhe, nlmodel.uop, y)
+    preparestate!(nmpc_mhe, [55, 30])
+    nmpc_mhe([55, 30])
+
+    empc = setconstraint!(
+        NonLinMPC(nlmodel, Mwt=[0, 0], Hp=10, Cwt=Inf, Ewt=1, JE=JE, p=R̂y), ymin=[45, -Inf]
+    )
+    preparestate!(empc, [55, 30])
+    empc()
+
+    linearizemodel = linearize(nlmodel)
+    setmodel!(mpc_kf, linearizemodel)
+
+end
+
+end # @setup_workload
\ No newline at end of file