diff --git a/benchmarks/README.md b/benchmarks/README.md
index 66669e1..7fede32 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -17,11 +17,10 @@ Exits nonzero if a gated metric exceeds its ceiling.
 Correctness tests do not catch cost regressions, so the suite measures two
 things that move silently:
 
-**Layout size and memory allocated by an operation.** Both deterministic and
-comparable across machines, so they are gated tightly. Memory uses
-`tracemalloc` rather than `ru_maxrss`, which is a process-lifetime high-water
-mark and reports zero for an operation staying under the peak set while
-building its input.
+**Layout size.** `bytes_per_nonzero` and `indices_bytes_per_nonzero` -- what
+the compressed format actually costs per stored value. Deterministic and
+comparable across machines, so they are gated tightly, and nothing else in
+the project guards them.
 
 **Throughput relative to scipy**, never absolute seconds. The same work is
 timed through scipy in the same process and the ratio recorded, which cancels
@@ -38,20 +37,29 @@ leak between them otherwise.
 metrics named in `margins` are gated; anything else a case returns is
 recorded for context.
 
-The checked-in ceilings were recorded before the memory fixes landed, so the
-memory ones are deliberately generous and should be re-recorded as those
-merge. On a 4M-nonzero array, for results of length `n_minor`:
-
-| metric | recorded | with the fix |
-|---|---|---|
-| `minor_sum_peak_mb` | 66 MB | 0.8 MB |
-| `minor_extrema_peak_mb` | 66 MB | 1.6 MB |
-| `minor_getnnz_peak_mb` | 32 MB | 0.8 MB |
-| `minor_selection_peak_mb` | 62 MB | 4.8 MB |
-| `misaligned_matmul_peak_mb` | 204 MB | 115 MB |
+Memory is no longer measured here; see below.
 
 ## Adding a case
 
 Write a function returning `{metric: value}` in `cases.py`, decorated with
 `@fast` (runs on every PR, keep it under a minute) or `@slow`. Add any new
 gated metric to `margins` in `baselines.json`, then `--record`.
+
+## Memory is a test, not a benchmark
+
+Memory is not measured here. `pytest-memray` ceilings in `tests/` assert it
+instead, because the claims are structural: "a minor-axis reduction must not
+allocate anything nnz-sized" either holds or it does not. A ceiling fails the
+moment it stops being true, where a recorded number only shows it drifting,
+against a baseline needing a re-record whenever the bound legitimately moves.
+
+Those tests pin `numba.set_num_threads`, so a ceiling means the same thing on
+a 4-core runner and a 96-core one. A recorded figure could not: the
+accumulators being bounded are sized by the runner's core count.
+
+Neither tool sees RSS, so neither catches allocator *fragmentation* -- many
+variably-sized alloc/free cycles driving the resident set far above the live
+set. Both report the live high-water mark, which stays small throughout such a
+run. That failure mode is real but not reliably gateable, since RSS moves with
+the allocator, the runner and the thread count; diagnose it with
+`/proc/self/status` `VmHWM` around a workload when it is suspected.
diff --git a/benchmarks/baselines.json b/benchmarks/baselines.json
index 251c274..31b3717 100644
--- a/benchmarks/baselines.json
+++ b/benchmarks/baselines.json
@@ -1,12 +1,10 @@
 {
-  "comment": "Ceilings a metric must stay under, regenerated with `python -m benchmarks.run --set fast --record`. Only metrics listed in `margins` are gated; the rest are recorded by cases for context. Margins are the slack applied to a measured value when recording: tight for deterministic layout/memory numbers, loose for timing ratios, which vary with the runner.",
+  "comment": "Ceilings a metric must stay under, regenerated with `python -m benchmarks.run --set fast --record`. Only metrics listed in `margins` are gated; the rest are recorded by cases for context. Margins are the slack applied to a measured value when recording: tight for deterministic layout numbers, loose for timing ratios, which vary with the runner.",
   "margins": {
     "bytes_per_nonzero": 1.1,
     "indices_bytes_per_nonzero": 1.1,
     "vs_scipy_ratio": 1.1,
-    "peak_alloc_mb": 2.0,
     "time_ratio_vs_scipy": 4.0,
-    "peak_alloc_mb_view": 2.0,
     "time_ratio_view_over_materialize": 4.0,
     "cpu_ratio_1t_cp10k_log1p": 1.5,
     "cpu_ratio_1t_parafac2": 1.5,
@@ -20,136 +18,71 @@
       "indices_bytes_per_nonzero": 4.4,
       "vs_scipy_ratio": 0.4751
     },
-    "minor_sum_peak_mb": {
-      "peak_alloc_mb": 132.5131
-    },
-    "misaligned_matmul_peak_mb": {
-      "peak_alloc_mb": 407.2383
-    },
     "matvec_vs_scipy": {
       "time_ratio_vs_scipy": 1.0
     },
     "matmat_vs_scipy": {
       "time_ratio_vs_scipy": 1.0
     },
-    "minor_extrema_peak_mb": {
-      "peak_alloc_mb": 132.5453
-    },
-    "minor_getnnz_peak_mb": {
-      "peak_alloc_mb": 64.0324
-    },
-    "minor_selection_peak_mb": {
-      "peak_alloc_mb": 124.5457
-    },
     "normalized_cp10k_log1p_matmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 2.7262
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_cp10k_log1p_matvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.3858
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_cp10k_log1p_rmatmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 3.6521
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_cp10k_log1p_rmatvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.1321
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_parafac2_matmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 2.7262
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_parafac2_matvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.3858
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_parafac2_rmatmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 3.6521
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_parafac2_rmatvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.1321
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_pearson_matmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 2.7262
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_pearson_matvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.3858
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_pearson_rmatmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 3.6521
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_pearson_rmatvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.1321
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_raw_matmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 2.7262
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_raw_matvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.3858
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_raw_rmatmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 3.6521
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_raw_rmatvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.1321
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_scanpy_matmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 2.7262
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_scanpy_matvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.3858
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_scanpy_rmatmat_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 3.6521
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_scanpy_rmatvec_vs_materialize": {
-      "time_ratio_view_over_materialize": 0.3,
-      "peak_alloc_mb_view": 0.1321
-    },
-    "normalized_cp10k_log1p_matmat_vs_sparse": {
-      "peak_alloc_mb_view": 2.7262
-    },
-    "normalized_cp10k_log1p_matvec_vs_sparse": {
-      "peak_alloc_mb_view": 0.3858
-    },
-    "normalized_parafac2_matmat_vs_sparse": {
-      "peak_alloc_mb_view": 2.7262
-    },
-    "normalized_parafac2_matvec_vs_sparse": {
-      "peak_alloc_mb_view": 0.3858
-    },
-    "normalized_pearson_matmat_vs_sparse": {
-      "peak_alloc_mb_view": 2.7262
-    },
-    "normalized_pearson_matvec_vs_sparse": {
-      "peak_alloc_mb_view": 0.3858
-    },
-    "normalized_raw_matmat_vs_sparse": {
-      "peak_alloc_mb_view": 2.7262
-    },
-    "normalized_raw_matvec_vs_sparse": {
-      "peak_alloc_mb_view": 0.3858
-    },
-    "normalized_scanpy_matmat_vs_sparse": {
-      "peak_alloc_mb_view": 2.7262
-    },
-    "normalized_scanpy_matvec_vs_sparse": {
-      "peak_alloc_mb_view": 0.3858
+      "time_ratio_view_over_materialize": 0.3
     },
     "normalized_cpu_vs_sparse_1t": {
       "cpu_ratio_1t_cp10k_log1p": 7.815,
diff --git a/benchmarks/cases.py b/benchmarks/cases.py
index f813ab0..47443a8 100644
--- a/benchmarks/cases.py
+++ b/benchmarks/cases.py
@@ -9,7 +9,6 @@
     best_cpu_time,
     best_time,
     integer_counts_csr,
-    peak_alloc_mb,
     ratio_vs_scipy,
 )
 
@@ -46,75 +45,6 @@ def layout_bytes_per_nonzero() -> dict[str, float]:
     }
 
 
-# -- memory ceilings ---------------------------------------------------------
-
-
-@fast
-def minor_sum_peak_mb() -> dict[str, float]:
-    """Memory allocated by a minor-axis sum."""
-    from vsparse import VCSRArray
-
-    v = VCSRArray.from_scipy(integer_counts_csr(40_000, 2_000, density=0.05))
-    nnz_mb = v.nnz * 8 / 1e6
-    return {
-        "peak_alloc_mb": peak_alloc_mb(lambda: v.sum(axis=0)),
-        "expanded_nnz_mb": nnz_mb,  # what a per-nonzero temporary would cost
-    }
-
-
-@fast
-def misaligned_matmul_peak_mb() -> dict[str, float]:
-    """Memory allocated by the matmul direction the storage isn't aligned for."""
-    from vsparse import VCSCArray
-
-    v = VCSCArray.from_scipy(integer_counts_csr(40_000, 2_000, density=0.05))
-    rng = np.random.default_rng(0)
-    B = rng.normal(size=(v.shape[1], 4))
-    array_mb = (v.values.nbytes + v.value_ptr.nbytes + v.indices.nbytes) / 1e6
-
-    return {
-        "peak_alloc_mb": peak_alloc_mb(lambda: v.normalized() @ B),
-        "array_mb": array_mb,  # what a full second copy would cost
-    }
-
-
-@fast
-def minor_extrema_peak_mb() -> dict[str, float]:
-    """Memory allocated by a minor-axis max/min."""
-    from vsparse import VCSRArray
-
-    v = VCSRArray.from_scipy(integer_counts_csr(40_000, 2_000, density=0.05))
-    return {
-        "peak_alloc_mb": peak_alloc_mb(lambda: v.max(axis=0)),
-        "expanded_nnz_mb": v.nnz * 8 / 1e6,
-    }
-
-
-@fast
-def minor_getnnz_peak_mb() -> dict[str, float]:
-    """Memory allocated by a per-minor-index stored-element count."""
-    from vsparse import VCSRArray
-
-    v = VCSRArray.from_scipy(integer_counts_csr(40_000, 2_000, density=0.05))
-    return {
-        "peak_alloc_mb": peak_alloc_mb(lambda: v.getnnz(axis=0)),
-        "indices_nnz_mb": v.nnz * 8 / 1e6,
-    }
-
-
-@fast
-def minor_selection_peak_mb() -> dict[str, float]:
-    """Memory allocated by a minor-axis selection."""
-    from vsparse import VCSRArray
-
-    v = VCSRArray.from_scipy(integer_counts_csr(40_000, 2_000, density=0.05))
-    cols = np.arange(0, v.shape[1], 2)
-    return {
-        "peak_alloc_mb": peak_alloc_mb(lambda: v[:, cols]),
-        "indices_nnz_mb": v.nnz * 8 / 1e6,
-    }
-
-
 # -- throughput, relative to scipy -------------------------------------------
 
 
@@ -144,12 +74,12 @@ def matmat_vs_scipy() -> dict[str, float]:
 
 # -- normalized views (issue #40 recipes): view-op vs materialize-then-op ---
 #
-# For every recipe, the view-based matmul/matvec should cost less, both in
-# time and in peak allocation, than fully materializing the (dense,
-# implicit-zero-filling) normalized matrix and multiplying that -- the whole
-# point of a *view*. ``time_ratio_view_over_materialize`` < 1 and
-# ``peak_alloc_mb_view`` < ``peak_alloc_mb_materialize`` are the expectation
-# for every case below.
+# For every recipe, the view-based matmul/matvec should be faster than fully
+# materializing the (dense, implicit-zero-filling) normalized matrix and
+# multiplying that -- the whole point of a *view*.
+# ``time_ratio_view_over_materialize`` < 1 is the expectation for every case
+# below. The matching memory claim is asserted as a ceiling in
+# ``tests/test_minor_axis_matmul.py`` rather than recorded here.
 
 
 def _normalized_bench(recipe: str, *, vector: bool) -> Callable[[], dict[str, float]]:
@@ -170,8 +100,6 @@ def via_materialize() -> np.ndarray:
 
         return {
             "time_ratio_view_over_materialize": best_time(via_view) / best_time(via_materialize),
-            "peak_alloc_mb_view": peak_alloc_mb(via_view),
-            "peak_alloc_mb_materialize": peak_alloc_mb(via_materialize),
         }
 
     bench.__name__ = f"normalized_{recipe}_{'matvec' if vector else 'matmat'}_vs_materialize"
@@ -196,8 +124,6 @@ def via_materialize() -> np.ndarray:
 
         return {
             "time_ratio_view_over_materialize": best_time(via_view) / best_time(via_materialize),
-            "peak_alloc_mb_view": peak_alloc_mb(via_view),
-            "peak_alloc_mb_materialize": peak_alloc_mb(via_materialize),
         }
 
     bench.__name__ = f"normalized_{recipe}_{'rmatvec' if vector else 'rmatmat'}_vs_materialize"
@@ -278,8 +204,6 @@ def via_sparse() -> np.ndarray:
         # every case doubled the suite's runtime for a number nothing gates.
         return {
             "wall_ratio_view_over_sparse": best_time(via_view) / best_time(via_sparse),
-            "peak_alloc_mb_view": peak_alloc_mb(via_view),
-            "peak_alloc_mb_sparse_delta": peak_alloc_mb(lambda: _sparse_delta(nv, mat)),
         }
 
     bench.__name__ = f"normalized_{recipe}_{'matvec' if vector else 'matmat'}_vs_sparse"
@@ -368,7 +292,6 @@ def large_layout_and_matmul() -> dict[str, float]:
     return {
         "bytes_per_nonzero": stored / v.nnz,
         "time_ratio_vs_scipy": ratio_vs_scipy(lambda: v @ B, lambda: mat @ B),
-        "matmul_peak_alloc_mb": peak_alloc_mb(lambda: v @ B),
     }
 
 
diff --git a/benchmarks/harness.py b/benchmarks/harness.py
index ada58dc..4f2f7f9 100644
--- a/benchmarks/harness.py
+++ b/benchmarks/harness.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import time
-import tracemalloc
 from collections.abc import Callable
 from typing import Any
 
@@ -9,25 +8,6 @@
 import scipy.sparse as sp
 
 
-def peak_alloc_mb(fn: Callable[[], Any]) -> float:
-    """Peak memory allocated during ``fn``, in MB.
-
-    Not RSS, which is a process-lifetime high-water mark and so reports zero
-    for anything staying under the peak set while building its input. numpy
-    allocations are traced, numba's internal ones are not.
-    """
-    fn()  # JIT compile / warm caches outside the measurement
-    tracemalloc.start()
-    try:
-        before = tracemalloc.get_traced_memory()[0]
-        tracemalloc.reset_peak()
-        fn()
-        peak = tracemalloc.get_traced_memory()[1]
-    finally:
-        tracemalloc.stop()
-    return max(0.0, (peak - before) / 1e6)
-
-
 def best_time(fn: Callable[[], Any], repeat: int = 7) -> float:
     """Best wall-clock time over ``repeat`` runs, in seconds. Warms up first."""
     fn()  # JIT compile / allocate caches outside the measurement
diff --git a/pyproject.toml b/pyproject.toml
index d59859a..3b8a2e4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,6 +46,7 @@ dev = [
     "ty>=0.0.1a1",
     "hypothesis>=6.100",
     "codespell>=2.3",
+    "pytest-memray>=1.11.0",
 ]
 
 [build-system]
diff --git a/tests/conftest.py b/tests/conftest.py
index a2d81fd..7e0cce0 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -36,3 +36,22 @@ def csc(dense) -> sp.csc_array:
 @pytest.fixture
 def csr(dense) -> sp.csr_array:
     return sp.csr_array(dense)
+
+
+#: Threads the memory-limited tests run their kernels with. Their accumulators
+#: are ``nthreads * n_minor * width * 8`` bytes, so an unpinned count would make
+#: the same ceiling mean something different on every machine.
+MEMORY_TEST_THREADS = 4
+
+
+@pytest.fixture
+def pinned_threads():
+    """Pin numba's thread count so accumulator sizes are machine-independent."""
+    import numba
+
+    previous = numba.get_num_threads()
+    numba.set_num_threads(MEMORY_TEST_THREADS)
+    try:
+        yield MEMORY_TEST_THREADS
+    finally:
+        numba.set_num_threads(previous)
diff --git a/tests/test_minor_axis_matmul.py b/tests/test_minor_axis_matmul.py
index b558cb4..f8977e0 100644
--- a/tests/test_minor_axis_matmul.py
+++ b/tests/test_minor_axis_matmul.py
@@ -9,13 +9,12 @@
 
 from __future__ import annotations
 
-import tracemalloc
 
 import numpy as np
 import pytest
 import scipy.sparse as sp
 
-from vsparse import VCSCArray, VCSRArray
+from vsparse import RECIPES, VCSCArray, VCSRArray
 from vsparse._ops import accumulator_threads
 
 
@@ -93,29 +92,36 @@ def test_no_second_copy_of_the_array_is_built(vcls, rng):
     assert nv._arr is v
 
 
-def test_misaligned_matmul_peak_is_bounded_by_the_accumulator_budget(rng):
-    """Peak memory tracks the (fixed) accumulator budget, not the size of the array."""
-    dense = rng.integers(1, 5, size=(1200, 400)).astype(np.float64)
+@pytest.fixture(scope="module")
+def misaligned_setup():
+    """A 1_200 x 400 VCSC view and a width-2 operand, JIT warmed, built outside the body."""
+    rng = np.random.default_rng(0)
+    dense = rng.integers(1, 5, size=(1_200, 400)).astype(np.float64)
     v = VCSCArray.from_scipy(sp.csc_array(dense))
-    nnz_bytes = v.nnz * v.indices.dtype.itemsize
     B = rng.normal(size=(dense.shape[1], 2))
-
-    v.normalized() @ B  # warm up the JIT before measuring
-
-    nv = v.normalized()
-    tracemalloc.start()
-    try:
-        before = tracemalloc.get_traced_memory()[0]
-        tracemalloc.reset_peak()
-        out = nv @ B
-        peak = tracemalloc.get_traced_memory()[1]
-    finally:
-        tracemalloc.stop()
-
-    # Accumulator block is nthreads * n_rows * width * 8 bytes -- independent
-    # of nnz, so it stays far below a per-nonzero cost for this shape.
-    assert peak - before < nnz_bytes
-    np.testing.assert_allclose(out, _reference(dense) @ B, atol=1e-7)
+    for warm in RECIPES:
+        v.normalized(warm) @ B  # warm up the JIT for every recipe
+    return v, B, dense
+
+
+# 480_000 nonzeros: 3.8 MB of values, 1.9 MB of indices. The accumulator block
+# is `MEMORY_TEST_THREADS * 1_200 * 2 * 8` = 77 KB plus a 19 KB output, so this
+# ceiling is ~2x that and ~15x under the values array.
+@pytest.mark.parametrize("recipe", sorted(RECIPES))
+@pytest.mark.limit_memory("256 KB")
+def test_misaligned_matmul_peak_is_bounded_by_the_accumulator_budget(
+    pinned_threads, misaligned_setup, recipe
+):
+    """Peak memory tracks the accumulator budget, not the size of the array."""
+    v, B, _ = misaligned_setup
+    out = v.normalized(recipe) @ B
+    assert out.shape == (1_200, 2)
+
+
+def test_misaligned_matmul_result_is_still_correct(misaligned_setup):
+    """The bounded-memory path above must also produce the right numbers."""
+    v, B, dense = misaligned_setup
+    np.testing.assert_allclose(v.normalized() @ B, _reference(dense) @ B, atol=1e-7)
 
 
 def test_accumulator_threads_degrades_to_one_for_a_huge_output_axis():
diff --git a/tests/test_reduction_memory.py b/tests/test_reduction_memory.py
index 52a0239..64c35bd 100644
--- a/tests/test_reduction_memory.py
+++ b/tests/test_reduction_memory.py
@@ -1,6 +1,13 @@
-from __future__ import annotations
+"""Minor-axis reductions: correctness, and ceilings on what they allocate.
+
+The ``pytest-memray`` ceilings here assert a structural property -- a reduction
+producing an ``n_minor``-sized result must not allocate anything that grows
+with ``nnz``. They rely on ``pinned_threads`` to fix the thread count the
+accumulators are sized by, and on module-scoped fixtures for the inputs, since
+a ``limit_memory`` mark measures the test body alone.
+"""
 
-import tracemalloc
+from __future__ import annotations
 
 import numba
 import numpy as np
@@ -78,31 +85,55 @@ def test_accumulator_block_stays_within_budget(n_minor, bytes_per_element):
         assert nthreads * n_minor * bytes_per_element <= _ACCUMULATOR_BUDGET_BYTES
 
 
+@pytest.fixture(scope="module")
+def reduction_array():
+    """A 2_000 x 500 array with every reduction's JIT warmed, built outside the body."""
+    rng = np.random.default_rng(0)
+    dense = rng.integers(1, 5, size=(2_000, 500)).astype(np.float64)
+    v = VCSRArray.from_scipy(sp.csr_array(dense))
+    for warm in (v.sum, v.max, v.getnnz):
+        warm(axis=0)
+    return v
+
+
+# 1e6 nonzeros here: 8 MB of values, 4 MB of indices. The accumulator block is
+# `MEMORY_TEST_THREADS * 500 * 8` = 16 KB, doubled for the extrema kernels,
+# which carry two. The ceilings sit ~2x over that and orders of magnitude under
+# nnz-scale.
+#
+# Marks go on each `pytest.param`: pytest-memray reads them at collection, so
+# one applied from the test body is never seen and asserts nothing.
 @pytest.mark.parametrize(
     ("label", "call"),
     [
-        ("sum", lambda v: v.sum(axis=0)),
-        ("max", lambda v: v.max(axis=0)),
-        ("getnnz", lambda v: v.getnnz(axis=0)),
+        pytest.param("sum", lambda v: v.sum(axis=0), marks=pytest.mark.limit_memory("64 KB")),
+        pytest.param("max", lambda v: v.max(axis=0), marks=pytest.mark.limit_memory("96 KB")),
+        pytest.param("getnnz", lambda v: v.getnnz(axis=0), marks=pytest.mark.limit_memory("64 KB")),
     ],
 )
-def test_minor_axis_reductions_allocate_nothing_nnz_sized(label, call):
+def test_minor_axis_reductions_allocate_nothing_nnz_sized(
+    pinned_threads, reduction_array, label, call
+):
     """An n_minor-sized result must not cost nnz-sized scratch."""
-    rng = np.random.default_rng(0)
-    n_rows, n_cols = 2_000, 500
-    dense = rng.integers(1, 5, size=(n_rows, n_cols)).astype(np.float64)
-    v = VCSRArray.from_scipy(sp.csr_array(dense))
-
-    call(v)  # warm up the JIT before measuring
+    out = call(reduction_array)
+    assert out.shape == (500,)
 
-    tracemalloc.start()
-    try:
-        before = tracemalloc.get_traced_memory()[0]
-        tracemalloc.reset_peak()
-        out = call(v)
-        peak = tracemalloc.get_traced_memory()[1]
-    finally:
-        tracemalloc.stop()
 
-    assert peak - before < 1 << 20, f"{label} allocated {(peak - before) / 1e6:.1f} MB"
-    assert out.shape == (n_cols,)
+@pytest.fixture(scope="module")
+def selection_array():
+    """A 2_000 x 500 array with the selection kernels' JIT warmed."""
+    rng = np.random.default_rng(0)
+    dense = rng.integers(1, 5, size=(2_000, 500)).astype(np.float64)
+    v = VCSRArray.from_scipy(sp.csr_array(dense))
+    v[:, np.arange(0, 500, 2)]
+    return v
+
+
+# A selection's result does grow with what was selected: half of 1e6 nonzeros
+# is ~2.3 MB of output, which is the answer rather than scratch. The ceiling
+# sits above that and well below output-plus-an-nnz-sized-temporary.
+@pytest.mark.limit_memory("4 MB")
+def test_minor_axis_selection_allocates_no_nnz_sized_scratch(pinned_threads, selection_array):
+    """A selection may pay for its output, but not for a copy of the input."""
+    out = selection_array[:, np.arange(0, 500, 2)]
+    assert out.shape == (2_000, 250)
diff --git a/uv.lock b/uv.lock
index 06e08d6..ea0f251 100644
--- a/uv.lock
+++ b/uv.lock
@@ -590,6 +590,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/5b/058db09c45ba58a7321bdf2294cae651b37d6fec68117265af90cde043b0/legacy_api_wrap-1.5-py3-none-any.whl", hash = "sha256:5a8ea50e3e3bcbcdec3447b77034fd0d32cb2cf4089db799238708e4d7e0098d", size = 10182, upload-time = "2025-11-03T13:21:11.102Z" },
 ]
 
+[[package]]
+name = "linkify-it-py"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/45/98/7a1a5f31fd5c7ba93e963b168e244b8e3dd705b3d2a718e3c3307583bf57/linkify_it_py-2.2.0.tar.gz", hash = "sha256:907acd2d17ac1fbb9ddb62c8957ccbd6158cac602231a15c3b0cd1e215f03cee", size = 32939, upload-time = "2026-08-29T07:07:08.305Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/d4/1152d1c7ab42d8b908be64fd200ddc870dc9d4925e951198702084aa1a7d/linkify_it_py-2.2.0-py3-none-any.whl", hash = "sha256:3adc40eb5af300b2605fcfdb968c24e1d780a90f1f2221af7c15e5111e94d443", size = 21971, upload-time = "2026-08-29T07:07:07.164Z" },
+]
+
 [[package]]
 name = "llvmlite"
 version = "0.49.0"
@@ -627,6 +636,11 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/81/4da04ced5a082363ecfa159c010d200ecbd959ae410c10c0264a38cac0f5/markdown_it_py-4.2.0-py3-none-any.whl", hash = "sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a", size = 91687, upload-time = "2026-05-07T12:08:27.182Z" },
 ]
 
+[package.optional-dependencies]
+linkify = [
+    { name = "linkify-it-py" },
+]
+
 [[package]]
 name = "markupsafe"
 version = "3.0.3"
@@ -711,6 +725,61 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" },
 ]
 
+[[package]]
+name = "memray"
+version = "1.20.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "rich" },
+    { name = "textual" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/99/3b/8f9736cbf698e62cb7efb0e1715a30d6d06b3cffd980b435209eb522d5f8/memray-1.20.0.tar.gz", hash = "sha256:ce1f1d900948d57d7db5b5d8d81f4ebfcb798eff674493503ce5bfaafcea84dc", size = 2416480, upload-time = "2026-08-07T20:16:20.295Z" }
+wheels = [
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