diff --git a/figs/working_set/glm5_fp8_tp8_b300.png b/figs/working_set/glm5_fp8_tp8_b300.png
index 3e15512..dc2ca76 100644
Binary files a/figs/working_set/glm5_fp8_tp8_b300.png and b/figs/working_set/glm5_fp8_tp8_b300.png differ
diff --git a/scripts/working_set_analysis.py b/scripts/working_set_analysis.py
index a738ee3..5cc43ad 100644
--- a/scripts/working_set_analysis.py
+++ b/scripts/working_set_analysis.py
@@ -154,48 +154,49 @@ def plot(ws, hw, block_bytes, label, out_path):
     import matplotlib.pyplot as plt
 
     bgb = block_bytes / GB
-    taus = [r["tau"] for r in ws["taus"]]
-    peak_gb = np.array([r["peak_blocks"] * bgb for r in ws["taus"]])
+    pool = hw["kv_pool_gb"]                      # KV pool per node (= per replica)
+    gpr = hw["gpus_per_replica"]
+    node_lbl = f"1 node = {gpr}x {hw['gpu']} = {pool:.0f} GB KV"
+
+    # everything in node units: nodes = footprint_GB / pool
+    peak_nodes = np.array([r["peak_blocks"] * bgb / pool for r in ws["taus"]])
     apc = np.array([r["apc"] * 100 for r in ws["taus"]])
-    oracle_gb = ws["oracle_peak_blocks"] * bgb
+    oracle_nodes = ws["oracle_peak_blocks"] * bgb / pool
     ceil = ws["apc_ceiling"] * 100
-    pool = hw["kv_pool_gb"]                      # per replica
 
     fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
 
-    # --- panel 1: APC vs required KV footprint ---
-    ax1.plot(peak_gb, apc, "o-", color="#1f77b4", lw=2, ms=7, label="TTL-LRU W(T)")
-    for r, x, y in zip(ws["taus"], peak_gb, apc):
+    # --- panel 1: APC vs nodes of HBM needed ---
+    ax1.plot(peak_nodes, apc, "o-", color="#1f77b4", lw=2, ms=7, label="TTL-LRU W(T)")
+    for r, x, y in zip(ws["taus"], peak_nodes, apc):
         ax1.annotate(f"{r['tau']:g}s", (x, y), fontsize=8,
                      textcoords="offset points", xytext=(4, 5))
-    ax1.scatter([oracle_gb], [ceil], marker="*", s=320, color="#d62728", zorder=5,
-                label=f"oracle / ceiling ({ceil:.1f}%)")
+    ax1.scatter([oracle_nodes], [ceil], marker="*", s=320, color="#d62728", zorder=5,
+                label=f"oracle / ceiling ({ceil:.1f}% @ {oracle_nodes:.0f} nodes)")
     ax1.axhline(ceil, ls=":", color="#d62728", alpha=.5)
-    for k in (1, 2, 4, 8):
-        x = pool * k
-        ax1.axvline(x, ls="--", color="#2ca02c", alpha=.55)
-        ax1.text(x, 2, f"{k} replica\n{k*hw['gpus_per_replica']} GPU",
+    for k in (1, 2, 4, 8, 16, 32):
+        ax1.axvline(k, ls="--", color="#2ca02c", alpha=.45)
+        ax1.text(k, 2, f"{k} node / {k*gpr} GPU",
                  rotation=90, va="bottom", ha="right", fontsize=8, color="#2ca02c")
+    ax1.axvspan(0.1, 1, color="#2ca02c", alpha=.06)   # "fits in 1 node" region
     ax1.set_xscale("log")
-    ax1.set_xlabel("KV footprint that must be resident  (GB, log)")
+    ax1.set_xlabel(f"# nodes of GPU HBM that must hold the KV  ({node_lbl})")
     ax1.set_ylabel("Achievable prefix-cache hit rate  (APC %)")
-    ax1.set_title("APC vs KV-pool budget")
+    ax1.set_title("APC vs cluster size (nodes)")
     ax1.grid(alpha=.3, which="both"); ax1.legend(loc="lower right"); ax1.set_ylim(0, 100)
 
-    # --- panel 2: footprint over time for a few T ---
-    span = ws["span"]; grid = np.linspace(0, span, 400)
-    # recompute series for a representative subset from stored peaks is not enough;
-    # show peak/p50 bars instead (compact, robust)
+    # --- panel 2: nodes needed by retention window ---
     sel = [r for r in ws["taus"] if r["tau"] in (2, 30, 300, 600)]
     xs = np.arange(len(sel)); w = 0.38
-    ax2.bar(xs - w/2, [r["peak_blocks"]*bgb for r in sel], w, label="peak", color="#1f77b4")
-    ax2.bar(xs + w/2, [r["p50_blocks"]*bgb for r in sel], w, label="median", color="#aec7e8")
-    ax2.axhline(pool, ls="--", color="#2ca02c", lw=2, label=f"1 replica KV pool ({pool:.0f} GB)")
-    ax2.axhline(oracle_gb, ls=":", color="#d62728", lw=2, label=f"oracle full-ceiling ({oracle_gb:.0f} GB)")
+    ax2.bar(xs - w/2, [r["peak_blocks"]*bgb/pool for r in sel], w, label="peak", color="#1f77b4")
+    ax2.bar(xs + w/2, [r["p50_blocks"]*bgb/pool for r in sel], w, label="median", color="#aec7e8")
+    ax2.axhline(1, ls="--", color="#2ca02c", lw=2, label="your budget: 1 node")
+    ax2.axhline(oracle_nodes, ls=":", color="#d62728", lw=2,
+                label=f"oracle full-ceiling ({oracle_nodes:.0f} nodes)")
     ax2.set_xticks(xs); ax2.set_xticklabels([f"T={r['tau']:g}s\nAPC={r['apc']*100:.0f}%" for r in sel])
-    ax2.set_ylabel("KV footprint (GB)")
+    ax2.set_ylabel("# nodes of HBM needed")
     ax2.set_yscale("log")
-    ax2.set_title("Footprint by retention window vs pool")
+    ax2.set_title("Cluster size by retention window")
     ax2.grid(alpha=.3, axis="y", which="both"); ax2.legend(loc="upper left", fontsize=9)
 
     fig.suptitle(label, fontsize=13, fontweight="bold")
@@ -230,7 +231,7 @@ def main():
     gpus_per_replica = a.tp * a.pp
     total_hbm = gpus_per_replica * GPU_HBM_GB[a.gpu]
     kv_pool_gb = total_hbm - a.weight_gb - a.activation_gb
-    hw = {"gpus_per_replica": gpus_per_replica, "kv_pool_gb": kv_pool_gb}
+    hw = {"gpus_per_replica": gpus_per_replica, "kv_pool_gb": kv_pool_gb, "gpu": a.gpu}
 
     taus = [1, 2, 5, 10, 30, 60, 300, 600, 1800]
     n, ids, ts = load_trace(a.trace, a.min_ts, a.max_ts)