diff --git a/tests/analytical_visualization/app.py b/tests/analytical_visualization/app.py
index df07fcb..b151e3c 100644
--- a/tests/analytical_visualization/app.py
+++ b/tests/analytical_visualization/app.py
@@ -1685,6 +1685,35 @@ def _render_auto_explore_tab():
     _df = pd.DataFrame(_rows)
     st.dataframe(_df, width='stretch', hide_index=True)
 
+    # ── Top-N by memory (smallest → largest) ─────────────────────
+    st.markdown(
+        "**Top 10 configurations by memory footprint** "
+        "(smallest HBM% first). Useful when memory pressure is the "
+        "real binding constraint of your deployment."
+    )
+    _pareto_by_mem = sorted(
+        _res.pareto_scores,
+        key=lambda s: (s.hbm_utilization, s.pes_used, s.total_latency_ns),
+    )[:10]
+    _mem_rows = []
+    for i, s in enumerate(_pareto_by_mem):
+        _mem_rows.append({
+            "#": i,
+            "HBM %": round(s.hbm_utilization * 100, 1),
+            "weights (GB)": round(s.weights_gb, 2),
+            "KV (GB)": round(s.kv_gb, 2),
+            "PEs": s.pes_used,
+            "SIPs": s.sips_used,
+            "lat (ms)": round(s.latency_ms, 3),
+            "CP": s.cp, "TP": s.tp, "PP": s.pp, "DP": s.dp,
+            "kv": s.kv_shard_mode,
+            "ffn": s.ffn_shard_scope,
+            "tp_place": s.tp_placement,
+            "cp_place": s.cp_placement,
+        })
+    _df_mem = pd.DataFrame(_mem_rows)
+    st.dataframe(_df_mem, width='stretch', hide_index=True)
+
     # ── Parallelism sensitivity subplots ──────────────────────
     st.markdown(
         "**Parallelism sensitivity** — for the *baseline* config picked "