diff --git a/test_runner/performance/test_gist_build.py b/test_runner/performance/test_gist_build.py
new file mode 100644
index 0000000000..040adb1242
--- /dev/null
+++ b/test_runner/performance/test_gist_build.py
@@ -0,0 +1,48 @@
+import os
+from contextlib import closing
+from fixtures.zenith_fixtures import PostgresFactory, ZenithPageserver
+
+pytest_plugins = ("fixtures.zenith_fixtures", "fixtures.benchmark_fixture")
+
+#
+# Test buffering GisT build. It WAL-logs the whole relation, in 32-page chunks.
+# As of this writing, we're duplicate those giant WAL records for each page,
+# which makes the delta layer about 32x larger than it needs to be.
+#
+def test_gist_buffering_build(postgres: PostgresFactory, pageserver: ZenithPageserver, pg_bin, zenith_cli, zenbenchmark, repo_dir: str):
+    # Create a branch for us
+    zenith_cli.run(["branch", "test_gist_buffering_build", "empty"])
+
+    pg = postgres.create_start('test_gist_buffering_build')
+    print("postgres is running on 'test_gist_buffering_build' branch")
+
+    # Open a connection directly to the page server that we'll use to force
+    # flushing the layers to disk
+    psconn = pageserver.connect();
+    pscur = psconn.cursor()
+
+    # Get the timeline ID of our branch. We need it for the 'do_gc' command
+    with closing(pg.connect()) as conn:
+        with conn.cursor() as cur:
+            cur.execute("SHOW zenith.zenith_timeline")
+            timeline = cur.fetchone()[0]
+
+            # Create test table.
+            cur.execute("create table gist_point_tbl(id int4, p point)");
+            cur.execute("insert into gist_point_tbl select g, point(g, g) from generate_series(1, 1000000) g;");
+
+            # Build the index.
+            with zenbenchmark.record_pageserver_writes(pageserver, 'pageserver_writes'):
+                with zenbenchmark.record_duration('build'):
+                    cur.execute("create index gist_pointidx2 on gist_point_tbl using gist(p) with (buffering = on)");
+
+                    # Flush the layers from memory to disk. This is included in the reported
+                    # time and I/O
+                    pscur.execute(f"do_gc {pageserver.initial_tenant} {timeline} 1000000")
+
+            # Record peak memory usage
+            zenbenchmark.record("peak_mem", zenbenchmark.get_peak_mem(pageserver) / 1024, 'MB')
+
+            # Report disk space used by the repository
+            timeline_size = zenbenchmark.get_timeline_size(repo_dir, pageserver.initial_tenant, timeline)
+            zenbenchmark.record('size', timeline_size / (1024*1024), 'MB')