diff --git a/examples/next/swm/config.py b/examples/next/swm/config.py
new file mode 100644
index 0000000000..30679c5271
--- /dev/null
+++ b/examples/next/swm/config.py
@@ -0,0 +1,42 @@
+# GT4Py - GridTools Framework
+#
+# Copyright (c) 2014-2024, ETH Zurich
+# All rights reserved.
+#
+# Please, refer to the LICENSE file in the root directory.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import argparse
+
+parser = argparse.ArgumentParser(description="Shallow Water Model")
+parser.add_argument("--M", type=int, default=16, help="Number of points in the x direction")
+parser.add_argument("--N", type=int, default=16, help="Number of points in the y direction")
+parser.add_argument("--L_OUT", type=bool, default=True, help="a boolean for L_OUT")
+parser.add_argument("--ITMAX", type=int, default=4000, help="Number of iterations")
+parser.add_argument("--VAL_DEEP", type=bool, default=True, help="Do deep validation")
+parser.add_argument("--backend", type=str, default="gtfn_cpu", help="Backend to use")
+
+
+args = parser.parse_args()
+
+# Initialize model parameters
+backend = args.backend
+M = args.M
+N = args.N
+M_LEN = M + 1
+N_LEN = N + 1
+L_OUT = args.L_OUT
+VAL = True
+VAL_DEEP = False  # args.VAL_DEEP
+VIS = False
+VIS_DT = 100
+
+ITMAX = args.ITMAX
+dt = 90.0
+dt = dt
+dx = 100000.0
+dy = 100000.0
+fsdx = 4.0 / (dx)
+fsdy = 4.0 / (dy)
+a = 1000000.0
+alpha = 0.001
diff --git a/examples/next/swm/initial_conditions.py b/examples/next/swm/initial_conditions.py
new file mode 100644
index 0000000000..2981bb1e7c
--- /dev/null
+++ b/examples/next/swm/initial_conditions.py
@@ -0,0 +1,94 @@
+# GT4Py - GridTools Framework
+#
+# Copyright (c) 2014-2024, ETH Zurich
+# All rights reserved.
+#
+# Please, refer to the LICENSE file in the root directory.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+import array_api_compat
+
+
+def initialize_interior(xp, M, N, dx, dy, a):
+    pi = 4.0 * xp.arctan(1.0)
+    tpi = 2.0 * pi
+    d_i = tpi / M
+    d_j = tpi / N
+    el = N * dx
+    pcf = (pi * pi * a * a) / (el * el)
+
+    psi = (
+        a
+        * xp.sin((xp.arange(0, M + 1).reshape(-1, 1) + 0.5) * d_i)
+        * xp.sin((xp.arange(0, N + 1) + 0.5) * d_j)
+    )
+    p = (
+        pcf
+        * (xp.cos(2.0 * xp.arange(0, M).reshape(-1, 1) * d_i) + xp.cos(2.0 * xp.arange(0, N) * d_j))
+        + 50000.0
+    )
+
+    u = -(psi[1:, 1:] - psi[1:, :-1]) / dy
+    v = (psi[1:, 1:] - psi[:-1, 1:]) / dx
+
+    return u, v, p
+
+
+def apply_periodic_halo(arr, top=0, bottom=0, left=0, right=0):
+    """Apply periodic (wrap-around) halo padding to an array.
+
+    Parameters
+    ----------
+    arr : array
+        Input array to pad
+    top : int
+        Number of rows to add at the top (from bottom of array)
+    bottom : int
+        Number of rows to add at the bottom (from top of array)
+    left : int
+        Number of columns to add at the left (from right of array)
+    right : int
+        Number of columns to add at the right (from left of array)
+    """
+    xp = array_api_compat.array_namespace(arr)
+
+    # Build vertical padding from the original array before any modification
+    parts_v = []
+    if top > 0:
+        parts_v.append(arr[-top:, :])
+    parts_v.append(arr)
+    if bottom > 0:
+        parts_v.append(arr[:bottom, :])
+    arr = xp.concatenate(parts_v, axis=0)
+
+    # Build horizontal padding from the vertically-padded array
+    parts_h = []
+    if left > 0:
+        parts_h.append(arr[:, -left:])
+    parts_h.append(arr)
+    if right > 0:
+        parts_h.append(arr[:, :right])
+    arr = xp.concatenate(parts_h, axis=1)
+
+    return arr
+
+
+def initialize(xp, M, N, dx, dy, a):
+    u, v, p = initialize_interior(xp, M, N, dx, dy, a)
+
+    # Apply staggered 1-halo padding
+    u = apply_periodic_halo(u, top=1, right=1)
+    v = apply_periodic_halo(v, bottom=1, left=1)
+    p = apply_periodic_halo(p, bottom=1, right=1)
+
+    return u, v, p
+
+
+def initialize_2halo(xp, M, N, dx, dy, a):
+    u, v, p = initialize_interior(xp, M, N, dx, dy, a)
+    return (
+        apply_periodic_halo(u, 1, 1, 1, 1),
+        apply_periodic_halo(v, 1, 1, 1, 1),
+        apply_periodic_halo(p, 1, 1, 1, 1),
+    )
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diff --git a/examples/next/swm/ref/16x16/h.step0.t100.bin b/examples/next/swm/ref/16x16/h.step0.t100.bin
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diff --git a/examples/next/swm/ref/16x16/h.step1.t100.bin b/examples/next/swm/ref/16x16/h.step1.t100.bin
new file mode 100644
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new file mode 100644
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new file mode 100644
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+0.045935 0.137652 0.228048 0.316252 0.401414 0.482713 0.559365 0.630632 0.695827 0.754320 0.805548 0.849017 0.884307 0.911077 0.929070 0.938111 0.938113 0.929077 0.911089 0.884322 0.849035 0.805569 0.754342 0.695849 0.630654 0.559386 0.482731 0.401429 0.316264 0.228057 0.137656 0.045934 -0.046226 -0.137936 -0.228315 -0.316490 -0.401615 -0.482868 -0.559470 -0.630682 -0.695819 -0.754256 -0.805430 -0.848849 -0.884096 -0.910831 -0.928798 -0.937824 -0.937822 -0.928792 -0.910822 -0.884084 -0.848834 -0.805413 -0.754238 -0.695800 -0.630662 -0.559450 -0.482850 -0.401598 -0.316476 -0.228303 -0.137928 -0.046221 0.045935 
+0.047083 0.141086 0.233734 0.324136 0.411419 0.494743 0.573305 0.646347 0.713165 0.773116 0.825620 0.870171 0.906339 0.933776 0.952217 0.961483 0.961486 0.952224 0.933788 0.906355 0.870189 0.825640 0.773138 0.713188 0.646369 0.573325 0.494761 0.411434 0.324148 0.233742 0.141090 0.047082 -0.047374 -0.141370 -0.234001 -0.324374 -0.411620 -0.494899 -0.573409 -0.646397 -0.713158 -0.773052 -0.825502 -0.870003 -0.906128 -0.933530 -0.951945 -0.961196 -0.961194 -0.951940 -0.933521 -0.906116 -0.869988 -0.825484 -0.773033 -0.713138 -0.646376 -0.573389 -0.494880 -0.411602 -0.324359 -0.233989 -0.141362 -0.047370 0.047083 
+0.047776 0.143159 0.237168 0.328896 0.417461 0.502008 0.581723 0.655837 0.723636 0.784466 0.837740 0.882945 0.919644 0.947484 0.966195 0.975597 0.975600 0.966202 0.947495 0.919660 0.882964 0.837761 0.784488 0.723659 0.655859 0.581743 0.502026 0.417476 0.328908 0.237176 0.143163 0.047776 -0.048067 -0.143444 -0.237434 -0.329134 -0.417661 -0.502164 -0.581827 -0.655887 -0.723629 -0.784402 -0.837622 -0.882777 -0.919434 -0.947238 -0.965923 -0.975310 -0.975308 -0.965918 -0.947229 -0.919421 -0.882763 -0.837605 -0.784383 -0.723609 -0.655866 -0.581807 -0.502144 -0.417644 -0.329120 -0.237422 -0.143435 -0.048063 0.047776 
+0.048008 0.143853 0.238316 0.330488 0.419481 0.504437 0.584538 0.659010 0.727137 0.788261 0.841793 0.887217 0.924094 0.952068 0.970870 0.980317 0.980320 0.970877 0.952079 0.924109 0.887236 0.841814 0.788284 0.727160 0.659033 0.584558 0.504456 0.419497 0.330500 0.238324 0.143857 0.048008 -0.048299 -0.144137 -0.238582 -0.330726 -0.419682 -0.504593 -0.584642 -0.659060 -0.727130 -0.788198 -0.841676 -0.887049 -0.923883 -0.951822 -0.970598 -0.980030 -0.980028 -0.970592 -0.951813 -0.923871 -0.887034 -0.841658 -0.788179 -0.727110 -0.659040 -0.584622 -0.504574 -0.419665 -0.330711 -0.238571 -0.144129 -0.048295 0.048008 
+0.047776 0.143159 0.237168 0.328896 0.417461 0.502008 0.581723 0.655837 0.723636 0.784466 0.837740 0.882945 0.919645 0.947484 0.966195 0.975598 0.975600 0.966202 0.947496 0.919660 0.882964 0.837761 0.784488 0.723659 0.655859 0.581744 0.502026 0.417476 0.328908 0.237176 0.143163 0.047776 -0.048067 -0.143444 -0.237434 -0.329134 -0.417662 -0.502164 -0.581827 -0.655887 -0.723629 -0.784402 -0.837622 -0.882778 -0.919434 -0.947238 -0.965924 -0.975310 -0.975309 -0.965918 -0.947229 -0.919422 -0.882763 -0.837605 -0.784383 -0.723609 -0.655866 -0.581807 -0.502145 -0.417644 -0.329120 -0.237422 -0.143435 -0.048063 0.047776 
diff --git a/examples/next/swm/ref/64x64/z.step0.t100.bin b/examples/next/swm/ref/64x64/z.step0.t100.bin
new file mode 100644
index 0000000000..25896118ea
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diff --git a/examples/next/swm/ref/64x64/z.step1.t100.bin b/examples/next/swm/ref/64x64/z.step1.t100.bin
new file mode 100644
index 0000000000..d811cfc3e7
Binary files /dev/null and b/examples/next/swm/ref/64x64/z.step1.t100.bin differ
diff --git a/examples/next/swm/swm.py b/examples/next/swm/swm.py
new file mode 100644
index 0000000000..8a3e5415a0
--- /dev/null
+++ b/examples/next/swm/swm.py
@@ -0,0 +1,390 @@
+# GT4Py - GridTools Framework
+#
+# Copyright (c) 2014-2024, ETH Zurich
+# All rights reserved.
+#
+# Please, refer to the LICENSE file in the root directory.
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+This version uses 2 halo lines (1 on each side)
+
+e.g. for M=3, N=3, with 'x' = interior, '0' = periodic halo, the grid is:
+
+for all fields
+0 0 0 0 0
+0 x x x 0
+0 x x x 0
+0 x x x 0
+0 0 0 0 0
+"""
+
+from gt4py import next as gtx
+from gt4py.next import common as gtx_common
+from time import perf_counter
+import initial_conditions
+import utils
+import config
+from gt4py.next.otf import compiled_program
+from gt4py.next.program_processors.runners.dace import run_dace_gpu_cached, run_dace_cpu_cached
+
+# from gt4py.next.program_processors.runners import jax_jit
+import numpy as np
+
+try:
+    import cupy as cp
+except ImportError:
+    cp = None
+
+try:
+    import jax.numpy as jnp
+    import jax
+except ImportError:
+    jnp = None
+    jax = None
+
+dtype = gtx.float64
+
+BACKENDS = {
+    "gtfn_gpu": (gtx.gtfn_gpu, gtx.gtfn_gpu),
+    "gtfn_cpu": (gtx.gtfn_cpu, gtx.gtfn_cpu),
+    "dace_gpu": (run_dace_gpu_cached, run_dace_gpu_cached),
+    "dace_cpu": (run_dace_cpu_cached, run_dace_cpu_cached),
+    "numpy": (None, np),
+    "jnp": (None, jnp),
+    "jax_jit": (jax.jit, jnp),
+}
+if cp is not None:
+    BACKENDS["cupy"] = (None, cp)
+if jnp is not None:
+    assert jax is not None
+    BACKENDS["jax"] = (None, jnp)
+    BACKENDS["jax_jit"] = (jax.jit, jnp)
+
+
+allocator = None
+
+if config.backend not in BACKENDS:
+    raise ValueError(
+        f"Unsupported backend '{config.backend}'. Supported backends are: {list(BACKENDS.keys())}"
+    )
+backend, allocator = BACKENDS[config.backend]
+
+print(f"Using backend '{getattr(backend, 'name', backend)}'.")
+
+I = gtx.Dimension("I")
+J = gtx.Dimension("J")
+
+IJField = gtx.Field[gtx.Dims[I, J], dtype]
+
+
+@gtx.field_operator
+def avg_x(f: IJField):
+    """Average field in the x direction."""
+    return 0.5 * (f(I + 1) + f)
+
+
+@gtx.field_operator
+def avg_y(f: IJField):
+    """Average field in the y direction."""
+    return 0.5 * (f(J + 1) + f)
+
+
+@gtx.field_operator
+def avg_x_staggered(f: IJField):
+    """Average field which is staggered in x in the x direction."""
+    return 0.5 * (f(I - 1) + f)
+
+
+@gtx.field_operator
+def avg_y_staggered(f: IJField):
+    """Average field which is staggered in y in the y direction."""
+    return 0.5 * (f(J - 1) + f)
+
+
+@gtx.field_operator
+def delta_x(dx: dtype, f: IJField):
+    """Calculate the difference in the x direction."""
+    return (1.0 / dx) * (f(I + 1) - f)
+
+
+@gtx.field_operator
+def delta_y(dx: dtype, f: IJField):
+    """Calculate the difference in the y direction."""
+    return (1.0 / dx) * (f(J + 1) - f)
+
+
+@gtx.field_operator
+def delta_x_staggered(dx: dtype, f: IJField):
+    """Calculate the difference in the x direction for field staggered in x."""
+    return (1.0 / dx) * (f - f(I - 1))
+
+
+@gtx.field_operator
+def delta_y_staggered(dx: dtype, f: IJField):
+    """Calculate the difference in the y direction for field staggered in y."""
+    return (1.0 / dx) * (f - f(J - 1))
+
+
+@gtx.field_operator
+def timestep(
+    u: IJField,
+    v: IJField,
+    p: IJField,
+    dx: dtype,
+    dy: dtype,
+    dt: dtype,
+    uold: IJField,
+    vold: IJField,
+    pold: IJField,
+    alpha: dtype,
+) -> tuple[IJField, IJField, IJField, IJField, IJField, IJField]:
+    cu = avg_x(p) * u
+    cv = avg_y(p) * v
+    z = (delta_x(dx, v) - delta_y(dy, u)) / avg_x(avg_y(p))
+    h = p + 0.5 * (avg_x_staggered(u * u) + avg_y_staggered(v * v))
+
+    unew = uold + avg_y_staggered(z) * avg_y_staggered(avg_x(cv)) * dt - delta_x(dx, h) * dt
+    vnew = vold - avg_x_staggered(z) * avg_x_staggered(avg_y(cu)) * dt - delta_y(dy, h) * dt
+    pnew = pold - delta_x_staggered(dx, cu) * dt - delta_y_staggered(dy, cv) * dt
+
+    uold_new = u + alpha * (unew - 2.0 * u + uold)
+    vold_new = v + alpha * (vnew - 2.0 * v + vold)
+    pold_new = p + alpha * (pnew - 2.0 * p + pold)
+
+    return (
+        unew,
+        vnew,
+        pnew,
+        uold_new,
+        vold_new,
+        pold_new,
+    )
+
+
+@gtx.program(backend=backend)
+def timestep_program(
+    u: IJField,
+    v: IJField,
+    p: IJField,
+    dx: dtype,
+    dy: dtype,
+    dt: dtype,
+    uold: IJField,
+    vold: IJField,
+    pold: IJField,
+    alpha: dtype,
+    unew: IJField,
+    vnew: IJField,
+    pnew: IJField,
+    M: gtx.int32,
+    N: gtx.int32,
+):
+    timestep(
+        u=u,
+        v=v,
+        p=p,
+        dx=dx,
+        dy=dy,
+        dt=dt,
+        uold=uold,
+        vold=vold,
+        pold=pold,
+        alpha=alpha,
+        out=(unew, vnew, pnew, uold, vold, pold),
+        domain={I: (0, M), J: (0, N)},
+    )
+
+
+def apply_periodicity(x: IJField):
+    """Apply periodicity to the field x."""
+    return gtx_common._field(
+        x.array_ns.pad(x.ndarray[1:-1, 1:-1], ((1, 1), (1, 1)), mode="wrap"),
+        domain=x.domain,
+        dtype=x.dtype,
+    )
+
+
+def apply_periodicity_jax(x: IJField):
+    """Apply periodicity to the field x."""
+    return gtx_common._field(
+        x.array_ns.pad(x.ndarray, ((1, 1), (1, 1)), mode="wrap"),
+        domain=gtx.domain({I: (-1, x.shape[0] + 1), J: (-1, x.shape[1] + 1)}),
+        dtype=x.dtype,
+    )
+
+
+def main():
+    dt0 = 0.0
+    dt25 = 0.0
+    dt3 = 0.0
+
+    M = config.M
+    N = config.N
+
+    domain = gtx.domain({I: (-1, M + 1), J: (-1, N + 1)})
+
+    pnew = gtx.empty(domain, dtype=dtype, allocator=allocator)
+    unew = gtx.empty(domain, dtype=dtype, allocator=allocator)
+    vnew = gtx.empty(domain, dtype=dtype, allocator=allocator)
+
+    # Initialize fields
+    _u, _v, _p = initial_conditions.initialize_2halo(np, M, N, config.dx, config.dy, config.a)
+    u = gtx.as_field(domain, _u, dtype=dtype, allocator=allocator)
+    v = gtx.as_field(domain, _v, dtype=dtype, allocator=allocator)
+    p = gtx.as_field(domain, _p, dtype=dtype, allocator=allocator)
+
+    # Initial old fields
+    uold = gtx.as_field(domain, _u, dtype=dtype, allocator=allocator)
+    vold = gtx.as_field(domain, _v, dtype=dtype, allocator=allocator)
+    pold = gtx.as_field(domain, _p, dtype=dtype, allocator=allocator)
+
+    # Print initial conditions
+    if config.L_OUT:
+        print(" Number of points in the x direction: ", M)
+        print(" Number of points in the y direction: ", N)
+        print(" grid spacing in the x direction: ", config.dx)
+        print(" grid spacing in the y direction: ", config.dy)
+        print(" time step: ", config.dt)
+        print(" time filter coefficient: ", config.alpha)
+
+        print(" Initial p:\n", p[:, :].ndarray.diagonal()[1:-1])
+        print(" Initial u:\n", u[:, :].ndarray.diagonal()[1:-1])
+        print(" Initial v:\n", v[:, :].ndarray.diagonal()[1:-1])
+
+    USE_PROGRAM = True
+
+    if backend == jax.jit:
+        prog = timestep.with_backend(backend)
+    elif backend is not None:
+        if USE_PROGRAM:
+            prog = timestep_program.with_backend(backend).compile(offset_provider={}, M=[M], N=[N])
+        else:
+            prog = timestep.with_backend(backend).compile(offset_provider={})
+        gtx.wait_for_compilation()
+    else:
+        prog = timestep_program if USE_PROGRAM else timestep
+
+    t0_start = perf_counter()
+
+    # Main time loop
+    for ncycle in range(config.ITMAX):
+        if (ncycle % 100 == 0) & (config.VIS == False):
+            print(f"cycle number{ncycle}")
+
+        if config.VAL_DEEP and ncycle <= 3:
+            print("validating init")
+            utils.validate_uvp(
+                u.asnumpy()[:-1, 1:],
+                v.asnumpy()[1:, :-1],
+                p.asnumpy()[1:, 1:],
+                M,
+                N,
+                ncycle,
+                "init",
+            )
+
+        t3_start = perf_counter()
+        if backend == jax.jit:
+            unew, vnew, pnew, uold, vold, pold = prog(
+                u=u,
+                v=v,
+                p=p,
+                dx=config.dx,
+                dy=config.dy,
+                dt=config.dt if ncycle == 0 else config.dt * 2.0,
+                uold=uold,
+                vold=vold,
+                pold=pold,
+                alpha=config.alpha if ncycle > 0 else 0.0,
+            )
+        elif USE_PROGRAM:
+            prog(
+                u=u,
+                v=v,
+                p=p,
+                dx=config.dx,
+                dy=config.dy,
+                dt=config.dt if ncycle == 0 else config.dt * 2.0,
+                uold=uold,
+                vold=vold,
+                pold=pold,
+                alpha=config.alpha if ncycle > 0 else 0.0,
+                unew=unew,
+                vnew=vnew,
+                pnew=pnew,
+                M=M,
+                N=N,
+            )
+        else:
+            prog(
+                u=u,
+                v=v,
+                p=p,
+                dx=config.dx,
+                dy=config.dy,
+                dt=config.dt if ncycle == 0 else config.dt * 2.0,
+                uold=uold,
+                vold=vold,
+                pold=pold,
+                alpha=config.alpha if ncycle > 0 else 0.0,
+                offset_provider={},
+                out=(unew, vnew, pnew, uold, vold, pold),
+                domain={I: (0, M), J: (0, N)},
+            )
+
+        if hasattr(u.array_ns, "cuda"):
+            u.array_ns.cuda.runtime.deviceSynchronize()
+        t3_stop = perf_counter()
+        dt3 = dt3 + (t3_stop - t3_start)
+
+        t25_start = perf_counter()
+        if backend == jax.jit:
+            unew = apply_periodicity_jax(unew)
+            vnew = apply_periodicity_jax(vnew)
+            pnew = apply_periodicity_jax(pnew)
+        else:
+            unew = apply_periodicity(unew)
+            vnew = apply_periodicity(vnew)
+            pnew = apply_periodicity(pnew)
+        t25_stop = perf_counter()
+        dt25 = dt25 + (t25_stop - t25_start)
+
+        # swap x with xnew fields
+        u, unew = unew, u
+        v, vnew = vnew, v
+        p, pnew = pnew, p
+
+        if (config.VIS) & (ncycle % config.VIS_DT == 0):
+            utils.live_plot3(
+                u.asnumpy(),
+                v.asnumpy(),
+                p.asnumpy(),
+                "ncycle: " + str(ncycle),
+            )
+
+    t0_stop = perf_counter()
+    dt0 = dt0 + (t0_stop - t0_start)
+    # Print initial conditions
+    if config.L_OUT:
+        print("cycle number ", config.ITMAX)
+        print(" diagonal elements of p:\n", p[:, :].ndarray.diagonal()[:-1])
+        print(" diagonal elements of u:\n", u[:, :].ndarray.diagonal()[:-1])
+        print(" diagonal elements of v:\n", v[:, :].ndarray.diagonal()[:-1])
+    print("total: ", dt0)
+    print("t100+t200+t300: ", dt3)
+    print("t150+t250: ", dt25)
+
+    if config.VAL:
+        utils.final_validation(
+            u.asnumpy()[:-1, 1:],
+            v.asnumpy()[1:, :-1],
+            p.asnumpy()[1:, 1:],
+            ITMAX=config.ITMAX,
+            M=M,
+            N=N,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/next/swm/swm_array_api.py b/examples/next/swm/swm_array_api.py
new file mode 100644
index 0000000000..2ddca8e54d
--- /dev/null
+++ b/examples/next/swm/swm_array_api.py
@@ -0,0 +1,470 @@
+# GT4Py - GridTools Framework
+#
+# Copyright (c) 2014-2024, ETH Zurich
+# All rights reserved.
+#
+# Please, refer to the LICENSE file in the root directory.
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Shallow Water Model using the Python Array API standard.
+
+This implementation uses symmetric halo lines (1 on each side) for all fields.
+Periodic boundary conditions are applied via halo exchange.
+
+Compatible with any array library supporting the Array API standard:
+  numpy, jax.numpy, cupy, array_api_strict, etc.
+
+Usage:
+  python swm_array_api.py --array-library numpy
+  python swm_array_api.py --array-library jax
+  python swm_array_api.py --array-library torch
+  python swm_array_api.py --array-library cupy
+  python swm_array_api.py --strict             # validate compliance with array_api_strict wrapping
+  python swm_array_api.py --array-library jax --compile    # run with jax.jit
+  python swm_array_api.py --array-library torch --compile  # run with torch.compile
+  python swm_array_api.py --array-library torch --compile --device cuda  # torch.compile on GPU
+  python swm_array_api.py --array-library jax --compile --device cpu     # jax.jit on CPU
+"""
+
+import argparse
+from time import perf_counter
+from array_api_compat import array_namespace
+import initial_conditions
+
+
+def _get_array_module(name):
+    """Import and return the array module for the given library name."""
+    if name == "numpy":
+        import numpy
+
+        return numpy
+    elif name == "jax":
+        import jax.numpy
+
+        return jax.numpy
+    elif name == "torch":
+        import torch
+
+        return torch
+    elif name == "cupy":
+        import cupy
+
+        return cupy
+    elif name == "array_api_strict":
+        import array_api_strict
+
+        return array_api_strict
+    else:
+        raise ValueError(f"Unknown array library: {name}")
+
+
+def _to_numpy(arr):
+    """Convert array to numpy, handling GPU/CUDA tensors."""
+    import numpy as np
+
+    try:
+        import torch
+
+        if isinstance(arr, torch.Tensor):
+            return arr.detach().cpu().numpy()
+    except ImportError:
+        pass
+    return np.asarray(arr)
+
+
+def _interior_to_halo(xp, interior):
+    """Build (M+2, N+2) array from (M, N) interior with periodic halos.
+
+    Wraps the interior periodically: last col -> left halo, first col -> right halo,
+    last row -> top halo, first row -> bottom halo.
+    """
+    middle_rows = xp.concat([interior[:, -1:], interior, interior[:, :1]], axis=1)
+    return xp.concat([middle_rows[-1:, :], middle_rows, middle_rows[:1, :]], axis=0)
+
+
+# ---------------------------------------------------------------------------
+# Stencil building blocks following Sadourny (1975), J. Atm. Sci. 32:680–688.
+#
+# The model uses an Arakawa C-grid with four types of grid point:
+#   p-point  at (i,   j  )  —  pressure/height h
+#   u-point  at (i+½, j  )  —  zonal velocity u      (staggered ½-cell in x)
+#   v-point  at (i,   j+½)  —  meridional velocity v  (staggered ½-cell in y)
+#   ζ-point  at (i+½, j+½)  —  vorticity ζ            (staggered ½-cell in both)
+#
+# avg_x / delta_x shift the x-position by ½:   p ↔ u,  v ↔ ζ
+# avg_y / delta_y shift the y-position by ½:   p ↔ v,  u ↔ ζ
+#
+# Each function reduces shape by 1 in its operating dimension:
+#   avg_x(f) / delta_x(dx, f)  : (Mx, My) → (Mx-1, My)
+#   avg_y(f) / delta_y(dy, f)  : (Mx, My) → (Mx, My-1)
+#
+# For an (M+2, N+2) halo array the interior of any point type is at [1:M+1, 1:N+1].
+# After an x-stencil op the (M+1, …) result's interior slice depends on input type:
+#   p- or v-point input  →  u- or ζ-point output,  interior at [1:M+1, ...]
+#   u- or ζ-point input  →  p- or v-point output,  interior at [0:M,   ...]
+# After a y-stencil op:
+#   p- or u-point input  →  v- or ζ-point output,  interior at [..., 1:N+1]
+#   v- or ζ-point input  →  p- or u-point output,  interior at [..., 0:N  ]
+# ---------------------------------------------------------------------------
+
+
+def avg_x(f):
+    """avg_x(f)[i,j] = 0.5*(f[i+1,j] + f[i,j])  — reduces x by 1"""
+    return 0.5 * (f[1:, :] + f[:-1, :])
+
+
+def avg_y(f):
+    """avg_y(f)[i,j] = 0.5*(f[i,j+1] + f[i,j])  — reduces y by 1"""
+    return 0.5 * (f[:, 1:] + f[:, :-1])
+
+
+def delta_x(dx, f):
+    """delta_x(f)[i,j] = (1/dx)*(f[i+1,j] - f[i,j])  — reduces x by 1"""
+    return (1.0 / dx) * (f[1:, :] - f[:-1, :])
+
+
+def delta_y(dy, f):
+    """delta_y(f)[i,j] = (1/dy)*(f[i,j+1] - f[i,j])  — reduces y by 1"""
+    return (1.0 / dy) * (f[:, 1:] - f[:, :-1])
+
+
+def timestep(xp, u, v, p, uold, vold, pold, dx, dy, dt_val, alpha_val, M, N):
+    """Perform one timestep of the shallow water equations.
+
+    All fields have shape (M+2, N+2) with 1-wide symmetric halos.
+    Implements the Sadourny (1975) Arakawa C-grid scheme via direct array composition.
+    Grid-point types follow Sadourny's notation  (p-, u-, v-, ζ-points):
+
+      cu  = avg_x(p) * u          # u-point  (i+½, j)
+      cv  = avg_y(p) * v          # v-point  (i, j+½)
+      z   = (delta_x(v) - delta_y(u)) / avg_x(avg_y(p))  # ζ-point  (i+½, j+½)
+      h   = p + 0.5*(avg_x(u²) + avg_y(v²))              # p-point  (i, j)
+
+      unew  at u-point:  avg_y(z) [ζ→u] * avg_y(avg_x(cv)) [v→ζ→u] * dt
+                       − delta_x(h)     [p→u]                       * dt
+      vnew  at v-point: −avg_x(z) [ζ→v] * avg_x(avg_y(cu)) [u→ζ→v] * dt
+                       − delta_y(h)     [p→v]                       * dt
+      pnew  at p-point: −delta_x(cu)    [u→p]                       * dt
+                       − delta_y(cv)    [v→p]                       * dt
+
+    Each stencil function reduces shape by 1 only in its operating dimension, so
+    compositions chain directly without any intermediate _interior_to_halo calls.
+
+    Slice convention for extracting the (M, N) interior from a composed result
+    (see stencil-block header for the full rule):
+      p- or v-point input → x-result at u- or ζ-point: interior rows [1:M+1, ...]
+      u- or ζ-point input → x-result at p- or v-point: interior rows [0:M,   ...]
+      p- or u-point input → y-result at v- or ζ-point: interior cols [..., 1:N+1]
+      v- or ζ-point input → y-result at p- or u-point: interior cols [..., 0:N  ]
+    """
+    uu = u * u
+    vv = v * v
+    # cu at u-points: avg_x(p) [p→u] * u[0:M+1,:]  → shape (M+1, N+2)
+    cu = avg_x(p) * u[:-1, :]
+    # cv at v-points: avg_y(p) [p→v] * v[:,0:N+1]  → shape (M+2, N+1)
+    cv = avg_y(p) * v[:, :-1]
+
+    # z_wide at ζ-points (i+½, j+½), shape (M+1, N+1):
+    #   delta_x(v) [v→ζ]: shape (M+1, N+2); [:, :N+1] trims to (M+1, N+1)
+    #   delta_y(u) [u→ζ]: shape (M+2, N+1); [:M+1, :] trims to (M+1, N+1)
+    #   avg_x(avg_y(p)) [p→v→ζ]: shape (M+1, N+1)
+    z_wide = (delta_x(dx, v)[:, : N + 1] - delta_y(dy, u)[: M + 1, :]) / avg_x(
+        avg_y(p)
+    )  # (M+1, N+1), ζ-points
+
+    # h_wide at p-points covering x=1..M+1, y=1..N+1 (interior + right/top halo),
+    # shape (M+1, N+1).  The extended range is needed by the subsequent delta_x/delta_y.
+    #   p[1:M+2, 1:N+2]            : p-points at x=1..M+1, y=1..N+1
+    #   avg_x(uu) [u→p]: result[k] at x=k+1; slice [0:M+1, 1:N+2] → x=1..M+1
+    #   avg_y(vv) [v→p]: result[l] at y=l+1; slice [1:M+2, 0:N+1] → y=1..N+1
+    h_wide = p[1 : M + 2, 1 : N + 2] + 0.5 * (
+        avg_x(uu)[0 : M + 1, 1 : N + 2] + avg_y(vv)[1 : M + 2, 0 : N + 1]
+    )  # (M+1, N+1), p-points
+
+    # unew at interior u-points: all three terms evaluated at (i+½, j) for i=1..M, j=1..N.
+    #   avg_y(z_wide)    [ζ→u]: z_wide ζ-point → avg_y → u-point; interior at [1:M+1, 0:N]
+    #   avg_y(avg_x(cv)) [v→ζ→u]: cv v-point → avg_x →ζ-point → avg_y → u-point; same slice
+    #   delta_x(h_wide)  [p→u]: h_wide p-point at x=k+1 → delta_x → u-point at x=k+3/2;
+    #                           interior u at x=3/2..M+1/2 ↔ slice [0:M, 0:N]
+    unew_interior = (
+        uold
+        + avg_y(z_wide)[1 : M + 1, 0:N] * avg_y(avg_x(cv))[1 : M + 1, 0:N] * dt_val
+        - delta_x(dx, h_wide)[0:M, 0:N] * dt_val
+    )
+    # vnew at interior v-points: all three terms evaluated at (i, j+½) for i=1..M, j=1..N.
+    #   avg_x(z_wide)    [ζ→v]: z_wide ζ-point → avg_x → v-point; interior at [0:M, 1:N+1]
+    #   avg_x(avg_y(cu)) [u→ζ→v]: cu u-point → avg_y → ζ-point → avg_x → v-point; same slice
+    #   delta_y(h_wide)  [p→v]: h_wide p-point at y=l+1 → delta_y → v-point at y=l+3/2;
+    #                           interior v at y=3/2..N+1/2 ↔ slice [0:M, 0:N]
+    vnew_interior = (
+        vold
+        - avg_x(z_wide)[0:M, 1 : N + 1] * avg_x(avg_y(cu))[0:M, 1 : N + 1] * dt_val
+        - delta_y(dy, h_wide)[0:M, 0:N] * dt_val
+    )
+    # pnew at interior p-points: both terms evaluated at (i, j) for i=1..M, j=1..N.
+    #   delta_x(cu) [u→p]: cu u-point → delta_x → p-point; interior at [0:M, 1:N+1]
+    #   delta_y(cv) [v→p]: cv v-point → delta_y → p-point; interior at [1:M+1, 0:N]
+    pnew_interior = (
+        pold - delta_x(dx, cu)[0:M, 1 : N + 1] * dt_val - delta_y(dy, cv)[1 : M + 1, 0:N] * dt_val
+    )
+
+    # -- Time filter (update old fields) --
+    uold_new = u[1 : M + 1, 1 : N + 1] + alpha_val * (
+        unew_interior - 2.0 * u[1 : M + 1, 1 : N + 1] + uold
+    )
+    vold_new = v[1 : M + 1, 1 : N + 1] + alpha_val * (
+        vnew_interior - 2.0 * v[1 : M + 1, 1 : N + 1] + vold
+    )
+    pold_new = p[1 : M + 1, 1 : N + 1] + alpha_val * (
+        pnew_interior - 2.0 * p[1 : M + 1, 1 : N + 1] + pold
+    )
+
+    # Build full arrays with halos for all returned fields.
+    unew = _interior_to_halo(xp, unew_interior)
+    vnew = _interior_to_halo(xp, vnew_interior)
+    pnew = _interior_to_halo(xp, pnew_interior)
+
+    return unew, vnew, pnew, uold_new, vold_new, pold_new
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Shallow Water Model (Array API)")
+    parser.add_argument(
+        "--array-library",
+        type=str,
+        default="numpy",
+        choices=["numpy", "jax", "torch", "cupy", "array_api_strict"],
+        help="Array library to use",
+    )
+    parser.add_argument(
+        "--strict",
+        action="store_true",
+        help="Enable array-api-strict compliance checking via array_api_compat",
+    )
+    parser.add_argument("--M", type=int, default=16)
+    parser.add_argument("--N", type=int, default=16)
+    parser.add_argument("--ITMAX", type=int, default=4000)
+    parser.add_argument("--validate", action="store_true", help="Validate against reference data")
+    parser.add_argument("--validate-deep", action="store_true", help="Deep validation of each step")
+    parser.add_argument(
+        "--compile",
+        action="store_true",
+        help="Enable JIT compilation (jax.jit for jax, torch.compile for torch)",
+    )
+    parser.add_argument("--no-output", action="store_true", help="Suppress diagnostic output")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default=None,
+        choices=["cpu", "cuda"],
+        help="Device to run on (default: CPU for numpy/torch, GPU for jax if available)",
+    )
+    args = parser.parse_args()
+
+    M = args.M
+    N = args.N
+    ITMAX = args.ITMAX
+    L_OUT = not args.no_output
+
+    # Physical parameters
+    dt = 90.0
+    dx = 100000.0
+    dy = 100000.0
+    a = 1000000.0
+    alpha = 0.001
+
+    # Get the array module
+    if args.array_library == "jax":
+        import jax
+
+        jax.config.update("jax_enable_x64", True)
+        if args.device is not None:
+            jax_device_kind = "gpu" if args.device == "cuda" else "cpu"
+            jax.config.update("jax_default_device", jax.devices(jax_device_kind)[0])
+            print(f"JAX device: {jax_device_kind}")
+
+    lib = _get_array_module(args.array_library)
+
+    # Configure torch device
+    if args.array_library == "torch" and args.device is not None:
+        import torch
+
+        torch.set_default_device(args.device)
+        print(f"Torch device: {args.device}")
+    elif args.device == "cuda" and args.array_library not in ("jax", "torch", "cupy"):
+        print(f"Warning: --device cuda not supported for {args.array_library}")
+
+    if args.strict:
+        import array_api_strict
+
+        array_api_strict.set_array_api_strict_flags(api_version="2024.12")
+
+    if args.strict and args.array_library != "array_api_strict":
+        # Wrap the library arrays in array_api_strict for compliance testing
+        import array_api_strict
+
+        xp = array_api_strict
+        print(
+            f"Running with {args.array_library} arrays wrapped in array_api_strict for compliance checking"
+        )
+    elif args.strict:
+        import array_api_strict
+
+        xp = array_api_strict
+        print("Running with array_api_strict directly")
+    else:
+        xp = lib
+        # Get the array-api-compat namespace for the library
+        test_arr = lib.zeros((1,))
+        xp = array_namespace(test_arr)
+        print(f"Running with {args.array_library} via array_api_compat namespace")
+
+    # Initialize fields
+    u, v, p = initial_conditions.initialize_2halo(xp, M, N, dx, dy, a)
+
+    if args.strict and args.array_library != "array_api_strict":
+        # Convert to array_api_strict arrays
+        import array_api_strict
+        import numpy as np
+
+        u_np = np.asarray(u) if hasattr(u, "__array__") else u
+        v_np = np.asarray(v) if hasattr(v, "__array__") else v
+        p_np = np.asarray(p) if hasattr(p, "__array__") else p
+        u = array_api_strict.asarray(u_np, dtype=array_api_strict.float64)
+        v = array_api_strict.asarray(v_np, dtype=array_api_strict.float64)
+        p = array_api_strict.asarray(p_np, dtype=array_api_strict.float64)
+        xp = array_api_strict
+
+    uold = xp.asarray(u, copy=True)[1 : M + 1, 1 : N + 1]
+    vold = xp.asarray(v, copy=True)[1 : M + 1, 1 : N + 1]
+    pold = xp.asarray(p, copy=True)[1 : M + 1, 1 : N + 1]
+
+    if L_OUT:
+        print(f" Number of points in the x direction: {M}")
+        print(f" Number of points in the y direction: {N}")
+        print(f" grid spacing in the x direction: {dx}")
+        print(f" grid spacing in the y direction: {dy}")
+        print(f" time step: {dt}")
+        print(f" time filter coefficient: {alpha}")
+
+    # For validation, we need numpy
+    if args.validate or args.validate_deep:
+        import numpy as np
+
+    if args.validate_deep:
+        import sys
+
+        sys.path.insert(0, "/home/user/SWM/swm_python")
+        import utils
+
+    # Set up the timestep function, optionally with JIT compilation
+    def timestep_fn(u, v, p, uold, vold, pold, dt_val, alpha_val):
+        return timestep(xp, u, v, p, uold, vold, pold, dx, dy, dt_val, alpha_val, M, N)
+
+    if args.compile:
+        if args.array_library == "jax":
+            import jax
+
+            timestep_fn = jax.jit(timestep_fn)
+            print("JIT compilation enabled via jax.jit")
+        elif args.array_library == "torch":
+            import torch
+
+            timestep_fn = torch.compile(timestep_fn)
+            print("JIT compilation enabled via torch.compile")
+        else:
+            print(f"Warning: --compile has no effect for {args.array_library}")
+
+        # Warm-up call to trigger compilation before timing
+        print("Warm-up call...", end=" ", flush=True)
+        t_warmup_start = perf_counter()
+        _warmup_result = timestep_fn(u, v, p, uold, vold, pold, dt, 0.0)
+        if args.array_library == "jax":
+            _warmup_result[0].block_until_ready()
+        elif args.array_library == "torch" and args.device == "cuda":
+            torch.cuda.synchronize()
+        t_warmup_stop = perf_counter()
+        del _warmup_result
+        print(f"done ({t_warmup_stop - t_warmup_start:.3f}s)")
+
+    dt_total = 0.0
+    dt_compute = 0.0
+
+    t0_start = perf_counter()
+
+    for ncycle in range(ITMAX):
+        if ncycle % 100 == 0:
+            print(f"cycle number {ncycle}")
+
+        if args.validate_deep and ncycle <= 3:
+            import numpy as np
+
+            u_np = _to_numpy(u)
+            v_np = _to_numpy(v)
+            p_np = _to_numpy(p)
+            # Convert 2-halo to reference layout
+            utils.validate_uvp(
+                u_np[:-1, 1:],
+                v_np[1:, :-1],
+                p_np[1:, 1:],
+                M,
+                N,
+                ncycle,
+                "init",
+            )
+
+        tdt = dt if ncycle == 0 else dt * 2.0
+        alpha_val = alpha if ncycle > 0 else 0.0
+
+        t_start = perf_counter()
+        unew, vnew, pnew, uold, vold, pold = timestep_fn(u, v, p, uold, vold, pold, tdt, alpha_val)
+        t_stop = perf_counter()
+        dt_compute += t_stop - t_start
+
+        u = unew
+        v = vnew
+        p = pnew
+
+    # Synchronize device for accurate timing
+    if args.array_library == "jax":
+        u.block_until_ready()
+    elif args.array_library == "torch" and args.device == "cuda":
+        import torch
+
+        torch.cuda.synchronize()
+
+    t0_stop = perf_counter()
+    dt_total = t0_stop - t0_start
+
+    if L_OUT:
+        print(f"cycle number {ITMAX}")
+
+    print(f"total: {dt_total}")
+    print(f"compute: {dt_compute}")
+
+    if args.validate:
+        import numpy as np
+
+        u_np = _to_numpy(u)
+        v_np = _to_numpy(v)
+        p_np = _to_numpy(p)
+
+        # Convert to reference layout for validation
+        u_ref_layout = u_np[:-1, 1:]
+        v_ref_layout = v_np[1:, :-1]
+        p_ref_layout = p_np[1:, 1:]
+
+        sys_path_added = False
+        import sys
+
+        if "/home/user/SWM/swm_python" not in sys.path:
+            sys.path.insert(0, "/home/user/SWM/swm_python")
+            sys_path_added = True
+        import utils
+
+        utils.final_validation(u_ref_layout, v_ref_layout, p_ref_layout, ITMAX=ITMAX, M=M, N=N)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/next/swm/utils.py b/examples/next/swm/utils.py
new file mode 100644
index 0000000000..1abdba4da3
--- /dev/null
+++ b/examples/next/swm/utils.py
@@ -0,0 +1,121 @@
+# GT4Py - GridTools Framework
+#
+# Copyright (c) 2014-2024, ETH Zurich
+# All rights reserved.
+#
+# Please, refer to the LICENSE file in the root directory.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+from IPython.display import clear_output
+from matplotlib import pyplot as plt
+
+
+def _to_2d(x):
+    if x.ndim == 3:
+        assert x.shape[2] == 1
+        return x[:, :, 0]
+    return x
+
+
+def read_uvp(step, suffix, M, N):
+    u_file = f"ref/{M}x{N}/u.step{step}.{suffix}.bin"
+    v_file = f"ref/{M}x{N}/v.step{step}.{suffix}.bin"
+    p_file = f"ref/{M}x{N}/p.step{step}.{suffix}.bin"
+    u = np.fromfile(u_file).reshape(M + 1, N + 1)
+    v = np.fromfile(v_file).reshape(M + 1, N + 1)
+    p = np.fromfile(p_file).reshape(M + 1, N + 1)
+    return u, v, p
+
+
+def read_cucvzh(step, suffix, M, N):
+    cu_file = f"ref/{M}x{N}/cu.step{step}.{suffix}.bin"
+    cv_file = f"ref/{M}x{N}/cv.step{step}.{suffix}.bin"
+    z_file = f"ref/{M}x{N}/z.step{step}.{suffix}.bin"
+    h_file = f"ref/{M}x{N}/h.step{step}.{suffix}.bin"
+    cu = np.fromfile(cu_file).reshape(M + 1, N + 1)
+    cv = np.fromfile(cv_file).reshape(M + 1, N + 1)
+    z = np.fromfile(z_file).reshape(M + 1, N + 1)
+    h = np.fromfile(h_file).reshape(M + 1, N + 1)
+    return cu, cv, z, h
+
+
+def validate_uvp(u, v, p, M, N, step, suffix):
+    u, v, p = _to_2d(u), _to_2d(v), _to_2d(p)
+
+    u_ref, v_ref, p_ref = read_uvp(step, suffix, M, N)
+    np.testing.assert_allclose(u, u_ref)
+    np.testing.assert_allclose(v, v_ref)
+    np.testing.assert_allclose(p, p_ref)
+    print(f"step {step} {suffix} values are correct.")
+
+
+def validate_cucvzh(cu, cv, z, h, M, N, step, suffix):
+    cu, cv, z, h = _to_2d(cu), _to_2d(cv), _to_2d(z), _to_2d(h)
+
+    cu_ref, cv_ref, z_ref, h_ref = read_cucvzh(step, suffix, M, N)
+    np.testing.assert_allclose(cu, cu_ref)
+    np.testing.assert_allclose(cv, cv_ref)
+    np.testing.assert_allclose(z, z_ref)
+    np.testing.assert_allclose(h, h_ref)
+    print(f"step {step} {suffix} values are correct.")
+
+
+def live_plot_val(fu, fv, fp, title=""):
+    mxu = fu.max()
+    mxv = fv.max()
+    mxp = fp.max()
+    clear_output(wait=True)
+    fig, (ax1, ax2, ax3) = plt.subplots(figsize=(13, 3), ncols=3)
+
+    pos1 = ax1.imshow(fp, cmap="Blues", vmin=-mxp, vmax=mxp, interpolation="none")
+    ax1.set_title("p")
+    plt.colorbar(pos1, ax=ax1)
+    pos2 = ax2.imshow(fu, cmap="Reds", vmin=-mxu, vmax=mxu, interpolation="none")
+    ax2.set_title("u")
+    plt.colorbar(pos2, ax=ax2)
+    pos3 = ax3.imshow(fv, cmap="Greens", vmin=-mxv, vmax=mxv, interpolation="none")
+    ax3.set_title("v")
+    plt.colorbar(pos3, ax=ax3)
+
+    fig.suptitle(title)
+    plt.show()
+
+
+def live_plot3(fu, fv, fp, title=""):
+    clear_output(wait=True)
+    fig, (ax1, ax2, ax3) = plt.subplots(figsize=(13, 3), ncols=3)
+
+    pos1 = ax1.imshow(fp, cmap="Blues", vmin=49999, vmax=50001, interpolation="none")
+    ax1.set_title("p")
+    pos2 = ax2.imshow(fu, cmap="Reds", vmin=-1, vmax=1, interpolation="none")
+    ax2.set_title("u")
+    pos3 = ax3.imshow(fv, cmap="Greens", vmin=-1, vmax=1, interpolation="none")
+    ax3.set_title("v")
+
+    fig.suptitle(title)
+    # plt.xlabel('x')
+    # plt.ylabel('y')
+    plt.show()
+
+
+def final_validation(u, v, p, ITMAX, M, N):
+    u, v, p = _to_2d(u), _to_2d(v), _to_2d(p)
+
+    uref, vref, pref = read_uvp(ITMAX, "final", M, N)
+
+    uval = uref - u
+    vval = vref - v
+    pval = pref - p
+
+    uLinfN = np.linalg.norm(uval, np.inf)
+    vLinfN = np.linalg.norm(vval, np.inf)
+    pLinfN = np.linalg.norm(pval, np.inf)
+
+    # live_plot_val(uval, vval, pval, "Val")
+    print("uLinfN: ", uLinfN)
+    print("vLinfN: ", vLinfN)
+    print("pLinfN: ", pLinfN)
+    print("udiff max: ", uval.max())
+    print("vdiff max: ", vval.max())
+    print("pdiff max: ", pval.max())
diff --git a/src/gt4py/next/embedded/nd_array_field.py b/src/gt4py/next/embedded/nd_array_field.py
index fa99f5fabd..cacf253f83 100644
--- a/src/gt4py/next/embedded/nd_array_field.py
+++ b/src/gt4py/next/embedded/nd_array_field.py
@@ -1126,6 +1126,14 @@ class JaxArrayConnectivityField(NdArrayConnectivityField):
     common._field.register(jnp.ndarray, JaxArrayField.from_array)
     common._connectivity.register(jnp.ndarray, JaxArrayConnectivityField.from_array)
 
+    def _flatten(v: JaxArrayField):
+        return (v.ndarray,), v.domain
+
+    def _unflatten(aux_data, children):
+        return JaxArrayField(aux_data, children[0])
+
+    jax.tree_util.register_pytree_node(JaxArrayField, _flatten, _unflatten)
+
 
 def _broadcast(field: common.Field, new_dimensions: Sequence[common.Dimension]) -> common.Field:
     if field.domain.dims == new_dimensions:
diff --git a/src/gt4py/next/ffront/decorator.py b/src/gt4py/next/ffront/decorator.py
index a7b703564a..9550bec2ce 100644
--- a/src/gt4py/next/ffront/decorator.py
+++ b/src/gt4py/next/ffront/decorator.py
@@ -98,6 +98,10 @@ class _CompilableGTEntryPointMixin(Generic[ffront_stages.DSLDefinitionT]):
     def __gt_type__(self) -> ts.CallableType: ...
 
     def with_backend(self, backend: next_backend.Backend) -> Self:
+        if backend.__module__.startswith("jax"):
+            if not isinstance(self.definition_stage, ffront_stages.DSLFieldOperatorDef):
+                raise ValueError("JAX backend is only supported for field operators.")
+            return backend(self.definition_stage.definition)
         return dataclasses.replace(self, backend=backend)
 
     def with_compilation_options(
@@ -253,10 +257,11 @@ def __gt_type__(self) -> ts_ffront.ProgramType:
 
     # TODO(ricoh): linting should become optional, up to the backend.
     def __post_init__(self) -> None:
-        no_args_past = toolchain.ConcreteArtifact(
-            self.past_stage, arguments.CompileTimeArgs.empty()
-        )
-        _ = self._frontend_transforms.past_lint(no_args_past).data
+        if isinstance(self.backend, next_backend.Backend):
+            no_args_past = toolchain.ConcreteArtifact(
+                self.past_stage, arguments.CompileTimeArgs.empty()
+            )
+            _ = self._frontend_transforms.past_lint(no_args_past).data
 
     @property
     def __name__(self) -> str:
@@ -588,7 +593,8 @@ def from_function(
     # TODO(ricoh): linting should become optional, up to the backend.
     def __post_init__(self) -> None:
         """This ensures that DSL linting occurs at decoration time."""
-        _ = self.foast_stage
+        if isinstance(self.backend, next_backend.Backend):
+            _ = self.foast_stage
 
     @functools.cached_property
     def foast_stage(self) -> ffront_stages.FOASTOperatorDef:
@@ -639,10 +645,16 @@ def __gt_closure_vars__(self) -> dict[str, Any]:
         return self.foast_stage.closure_vars
 
     def __call__(self, *args: Any, enable_jit: bool | None = None, **kwargs: Any) -> Any:
+        if not next_embedded.context.within_valid_context() and (
+            self.backend is None or self.backend.__module__.startswith("jax")
+        ):
+            # TODO(havogt): only jax.jit for now (we are not setting up the embedded context and just call the operator as is)
+            return self.definition(*args, **kwargs)
         if not next_embedded.context.within_valid_context() and self.backend is not None:
             # non embedded execution
             offset_provider = {**kwargs.pop("offset_provider", {})}
             if "out" not in kwargs:
+                print("oh")
                 raise errors.MissingArgumentError(None, "out", True)
             out = kwargs.pop("out")
             if "domain" in kwargs:
diff --git a/src/gt4py/next/iterator/transforms/fuse_as_fieldop.py b/src/gt4py/next/iterator/transforms/fuse_as_fieldop.py
index ffad69c921..00d5d5ce9a 100644
--- a/src/gt4py/next/iterator/transforms/fuse_as_fieldop.py
+++ b/src/gt4py/next/iterator/transforms/fuse_as_fieldop.py
@@ -184,7 +184,7 @@ def fuse_as_fieldop(
     )  # to keep the tree small
     new_stencil = merge_let.MergeLet().visit(new_stencil)
     new_stencil = inline_lambdas.InlineLambdas.apply(
-        new_stencil, opcount_preserving=True, force_inline_lift_args=True
+        new_stencil, opcount_preserving=False, force_inline_lift_args=True
     )
     new_stencil = inline_lifts.InlineLifts().visit(new_stencil)
 
@@ -206,6 +206,7 @@ def _arg_inline_predicate(node: itir.Expr, shifts: set[tuple[itir.OffsetLiteral,
         is_applied_fieldop := cpm.is_applied_as_fieldop(node)
         and not cpm.is_call_to(node.fun.args[0], "scan")
     ) or cpm.is_call_to(node, "if_"):
+        return True
         # always inline arg if it is an applied fieldop with only a single arg
         if is_applied_fieldop and len(node.args) == 1:
             return True