diff --git a/tests/test_autobatching.py b/tests/test_autobatching.py
index 0da77371..65b7143a 100644
--- a/tests/test_autobatching.py
+++ b/tests/test_autobatching.py
@@ -185,6 +185,51 @@ def test_n_edges_scalers_batched(ar_double_sim_state: ts.SimState) -> None:
     assert all(v >= 0 for v in result)
 
 
+@pytest.fixture
+def mixed_many_sim_state(
+    ar_supercell_sim_state: ts.SimState, si_sim_state: ts.SimState
+) -> ts.SimState:
+    """Batched state of alternating large and small periodic systems."""
+    return ts.concatenate_states(
+        [ar_supercell_sim_state, si_sim_state, si_sim_state, ar_supercell_sim_state],
+        device=si_sim_state.device,
+    )
+
+
+@pytest.mark.parametrize("max_atoms_per_chunk", [1, 16, 40, 10_000])
+def test_n_edges_scalers_chunked_matches_single_pass(
+    mixed_many_sim_state: ts.SimState, max_atoms_per_chunk: int
+) -> None:
+    """Splitting the neighbor-list pass leaves every per-system edge count unchanged."""
+    state = mixed_many_sim_state
+    single = _n_edges_scalers(state, cutoff=5.0, max_atoms_per_chunk=state.n_atoms)
+    chunked = _n_edges_scalers(state, cutoff=5.0, max_atoms_per_chunk=max_atoms_per_chunk)
+    assert chunked == single
+
+
+@pytest.mark.parametrize("max_atoms_per_chunk", [1, 16, 40, 10_000])
+def test_n_edges_scalers_chunk_bounds(
+    mixed_many_sim_state: ts.SimState,
+    max_atoms_per_chunk: int,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Each pass holds whole systems and exceeds the bound only for a lone system."""
+    passes: list[tuple[int, int]] = []
+    real_nl = ts.autobatching.torchsim_nl
+
+    def recording_nl(**kwargs: Any) -> Any:
+        passes.append((int(kwargs["system_idx"].max()) + 1, kwargs["positions"].shape[0]))
+        return real_nl(**kwargs)
+
+    monkeypatch.setattr(ts.autobatching, "torchsim_nl", recording_nl)
+    state = mixed_many_sim_state
+    _n_edges_scalers(state, cutoff=5.0, max_atoms_per_chunk=max_atoms_per_chunk)
+
+    assert sum(n_sys for n_sys, _ in passes) == state.n_systems
+    assert sum(n_at for _, n_at in passes) == state.n_atoms
+    assert all(n_at <= max_atoms_per_chunk or n_sys == 1 for n_sys, n_at in passes)
+
+
 @pytest.mark.parametrize("items", [[], {}])
 def test_to_constant_volume_bins_empty_input(
     items: list[Any] | dict[int, float],
diff --git a/torch_sim/autobatching.py b/torch_sim/autobatching.py
index 86a3e4c5..6d997981 100644
--- a/torch_sim/autobatching.py
+++ b/torch_sim/autobatching.py
@@ -42,6 +42,11 @@
 # nvalchemiops kernels used by ORB v3) raise "Failed to allocate <n> bytes".
 DEFAULT_OOM_ERROR_MESSAGES = ("CUDA out of memory", "Failed to allocate")
 
+# Atoms per neighbor-list pass when computing n_edges scalers. Alchemiops holds
+# fixed-width buffers of ~4.4 KiB per atom at a 6 A cutoff, so this caps the
+# pass at about 1 GiB however large the state is.
+N_EDGES_MAX_ATOMS_PER_CHUNK = 250_000
+
 
 def to_constant_volume_bins(  # noqa: C901
     items: dict[int, float] | list[Any],
@@ -290,16 +295,37 @@ def determine_max_batch_size(
             torch.cuda.empty_cache()
 
 
-def _n_edges_scalers(state: SimState, cutoff: float) -> list[float]:
-    """Return per-system edge counts from the neighbor list as memory scalers."""
-    _, system_mapping, _ = torchsim_nl(
-        positions=state.positions,
-        cell=state.cell,
-        pbc=state.pbc,
-        cutoff=cutoff,
-        system_idx=state.system_idx,
-    )
-    return system_mapping.bincount(minlength=state.n_systems).float().tolist()
+def _n_edges_scalers(
+    state: SimState,
+    cutoff: float,
+    max_atoms_per_chunk: int = N_EDGES_MAX_ATOMS_PER_CHUNK,
+) -> list[float]:
+    """Return per-system edge counts from the neighbor list as memory scalers.
+
+    The neighbor list runs over contiguous runs of systems holding at most
+    ``max_atoms_per_chunk`` atoms, so peak memory does not grow with the size of
+    the state. A system larger than the bound runs alone. Edge counts do not
+    depend on other systems, so the result equals a single pass over the state.
+    """
+    n_atoms_per_system = state.n_atoms_per_system.tolist()
+    n_systems = len(n_atoms_per_system)
+    scalers: list[float] = []
+    s0 = a0 = 0
+    while s0 < n_systems:
+        s1, a1 = s0 + 1, a0 + n_atoms_per_system[s0]
+        while s1 < n_systems and a1 - a0 + n_atoms_per_system[s1] <= max_atoms_per_chunk:
+            a1 += n_atoms_per_system[s1]
+            s1 += 1
+        _, system_mapping, _ = torchsim_nl(
+            positions=state.positions[a0:a1],
+            cell=state.cell[s0:s1],
+            pbc=state.pbc,
+            cutoff=cutoff,
+            system_idx=state.system_idx[a0:a1] - s0,
+        )
+        scalers.extend(system_mapping.bincount(minlength=s1 - s0).float().tolist())
+        s0, a0 = s1, a1
+    return scalers
 
 
 def calculate_memory_scalers(