diff --git a/benchmarks/pcie_collectives/serving.csv b/benchmarks/pcie_collectives/serving.csv
new file mode 100644
index 00000000..8c81db98
--- /dev/null
+++ b/benchmarks/pcie_collectives/serving.csv
@@ -0,0 +1,353 @@
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+frank1,switched,"0,1,2,3",2,clustered_full,0.6,8,1330.8885458952948,68.02907114605333,14.649048864553313,19.63227665706052,2.8878787878787877,0.0
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,8,1378.0572081717687,69.12726771389175,14.416754441860466,19.903573454339195,2.4879175550817343,0.0017016449234259785
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,16,1901.9072557100326,53.677990507430394,18.55471426880935,35.5018261504748,1.9828594935551695,0.003652300949598247
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,16,1870.0454918215676,55.0111159376942,18.09374739914469,34.01639344262295,1.7967229216894365,0.0
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,16,1918.601053223188,53.52104815549216,18.61361284981685,35.87472527472527,1.9763465505386202,0.0
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,32,2535.998873902063,35.7199278510275,27.742532007683867,71.25027442371021,2.3407823152675435,0.01646542261251372
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,32,2468.204668473438,33.9162839170466,29.046698225433524,72.50635838150289,2.910030572135682,0.028901734104046242
+frank1,switched,"0,1,2,3",2,clustered_full,0.6,32,2432.619527765294,48.659115830880545,20.34853570024174,50.197421434327154,1.0780539557441648,0.5398871877518131
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+frank1,switched,"4,5,6,7",2,ordinary,0.8,16,1878.854764475149,56.73640032128226,17.543356547339325,33.109191430545955,1.6462376280416648,0.007601935038009675
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+frank2,switched,"0,1,2,3",2,clustered_full,0.8,1,361.50755230238275,147.78398033995003,6.756242551339878,2.479437516582648,4.163438577872115,0.0002653223666755107
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,1,357.30916238663,151.43170501250586,6.592484258933195,2.3842568617296735,3.642413257379596,0.0002589331952356292
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,8,1372.3252786782928,70.42088846244978,14.162844972160356,19.600222717149222,2.4430203399275565,0.0005567928730512249
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,8,1322.1768030889523,72.53976470012755,13.744415412972973,18.157297297297298,2.1943541835905767,0.0
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,8,1339.4699374799525,67.20710349247764,14.822644292298715,19.730455075845974,2.8609324758842445,0.0011668611435239206
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,16,1893.3203211358123,54.777889252222636,18.16860173801002,34.63779527559055,1.9347017433782996,0.01145311381531854
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,16,1893.1141408884332,54.26718750154182,18.355659627167633,34.60910404624278,1.965407950738024,0.000722543352601156
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,16,1899.4757907925868,53.83775383952292,18.489470912600144,35.182083029861616,1.966304942549699,0.0
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,32,2490.3367847308027,41.05316166896588,23.946747130850046,60.57402101241643,1.9056338028169013,0.33715377268385865
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,32,2491.5042778189713,40.97496661974321,24.056620765550242,60.43253588516746,1.9736905189979623,0.3406698564593301
+frank2,switched,"0,1,2,3",2,clustered_full,0.8,32,2389.713026400272,52.934010356652564,18.600890727407407,45.09777777777778,0.842639947926353,0.6666666666666666
+frank2,switched,"4,5,6,7",2,ordinary,0.6,1,357.1232971957928,142.96126752162223,6.984329496708722,2.499725726823917,4.961601755348327,0.0
+frank2,switched,"4,5,6,7",2,ordinary,0.6,1,349.15215902471857,140.57096591116445,7.103702404184101,2.5202231520223153,5.4198047419804745,0.0002789400278940028
+frank2,switched,"4,5,6,7",2,ordinary,0.6,1,357.7948337574639,143.54997905294607,6.954624681780935,2.4791040699262497,4.772193389784212,0.0
+frank2,switched,"4,5,6,7",2,ordinary,0.6,8,1318.1810102129477,66.81470776360291,14.912558620892018,19.605046948356808,2.93761009982384,0.0011737089201877935
+frank2,switched,"4,5,6,7",2,ordinary,0.6,8,1333.1602275501743,73.32533421089533,13.60142052513369,18.02245989304813,2.199116229244778,0.0
+frank2,switched,"4,5,6,7",2,ordinary,0.6,8,1319.2588745205833,72.22582827283962,13.808464365363736,18.240499457111834,2.390733433120152,0.0010857763300760044
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+frank2,switched,"4,5,6,7",2,ordinary,0.6,16,1930.357709141982,54.464193921160856,18.288549406047515,35.39308855291576,1.973418633988106,0.0007199424046076314
+frank2,switched,"4,5,6,7",2,ordinary,0.6,16,1903.8512462717013,53.75855453858817,18.52416165134938,35.31363967906638,1.9447891951085965,0.0
+frank2,switched,"4,5,6,7",2,ordinary,0.6,32,2538.0995538422135,35.87682262325285,27.61418136174863,70.96830601092896,2.3219178082191783,0.025136612021857924
+frank2,switched,"4,5,6,7",2,ordinary,0.6,32,2554.560857983746,36.15250983687725,27.40584406724512,70.46095444685466,2.335197245047564,0.02386117136659436
+frank2,switched,"4,5,6,7",2,ordinary,0.6,32,2493.1504728221084,39.2890178917317,25.17129046207585,63.41816367265469,1.8836917675089346,0.2085828343313373
+frank2,switched,"0,1,2,3",3,ordinary,0.6,1,369.16950777636333,144.05934668963124,6.930168657593903,2.5430048992923244,4.773543821448013,0.00027218290691344586
+frank2,switched,"0,1,2,3",3,ordinary,0.6,1,369.9981681867804,140.8837598622072,7.086776456164765,2.650431394377957,5.31858653311074,0.00027831895352073476
+frank2,switched,"0,1,2,3",3,ordinary,0.6,1,353.00408611141495,143.5497888351379,6.957061226714012,2.4687243922425566,4.908494946735865,0.0
+frank2,switched,"0,1,2,3",3,ordinary,0.6,8,1336.3924642990476,67.59911177744638,14.751016045823665,19.696635730858468,2.8253553814911516,0.001160092807424594
+frank2,switched,"0,1,2,3",3,ordinary,0.6,8,1294.9793256198107,71.91251023715003,13.874356625408941,18.117775354416576,2.419361440851412,0.0
+frank2,switched,"0,1,2,3",3,ordinary,0.6,8,1319.5305514188851,68.18589989942001,14.624033711903394,19.353076480736057,2.8886327618498777,0.0005750431282346176
+frank2,switched,"0,1,2,3",3,ordinary,0.6,16,1892.281975184928,54.62104888040678,18.219665766690596,34.706389088298636,1.919824025857425,0.002871500358937545
+frank2,switched,"0,1,2,3",3,ordinary,0.6,16,1884.2627911627685,52.69922280589704,18.90940456622024,35.786458333333336,2.0951804423748546,0.0
+frank2,switched,"0,1,2,3",3,ordinary,0.6,16,1896.6733840289908,52.22858216523397,19.033809640390388,35.974474474474476,2.0353338651400685,0.0
+frank2,switched,"0,1,2,3",3,ordinary,0.6,32,2566.5154857779185,36.23050829768784,27.390136865800866,70.82251082251082,2.3516017374779423,0.01406926406926407
+frank2,switched,"0,1,2,3",3,ordinary,0.6,32,2583.6608439127226,36.23044231222825,27.404327244588742,71.19588744588745,2.339574554845725,0.016233766233766232
+frank2,switched,"0,1,2,3",3,ordinary,0.6,32,2532.7604245517296,34.308991933796015,28.68375988114286,73.60571428571428,2.692327034988146,0.017142857142857144
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,1,374.1492704089187,142.1756992121426,7.022248913127413,2.611969111969112,4.886376172090458,0.0005515719801434088
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,1,351.62432512846067,140.60973917944068,7.099996715839375,2.5264919129949805,5.232013385387619,0.0
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,1,364.47121792852084,143.4695522979685,6.958412532385898,2.571194315386718,4.831647991254441,0.0
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,8,1309.8078757458213,68.14842135120782,14.629067013233602,19.12313003452244,2.777417962003454,0.0005753739930955121
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,8,1304.5526445802077,68.541647050436,14.537028081235697,19.041189931350115,2.6254834550923936,0.0
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,8,1337.9220612801248,67.04960641985998,14.867652191812866,19.827485380116958,2.831133597301657,0.0005847953216374269
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,16,1843.4515664986368,51.16928034719359,19.462407877394636,35.772413793103446,2.2267996738765525,0.006130268199233717
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,16,1933.5142097196392,54.620901872171316,18.253870252692032,35.458722182340274,1.9761840446605439,0.0014357501794687725
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,16,1931.3717042506175,54.26767232628102,18.374858121387284,35.3106936416185,1.9651772793053546,0.0
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,32,2487.147235192438,36.26996342306652,27.2092091827027,68.88864864864865,2.495588135478178,0.10054054054054054
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,32,2378.3482610590013,55.404681067543436,17.953949631280963,42.75583864118896,0.45904867256637166,0.6341118188251946
+frank2,switched,"4,5,6,7",3,clustered_full,0.6,32,2442.293161968592,50.07173754896806,19.81404281127643,48.56147220046985,0.8898296765326658,0.5755677368833203
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,1,353.55923318564186,142.29579919110157,7.015941378892257,2.493248828878479,5.091485257646735,0.0
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,1,360.1620637973273,143.27692085421376,6.968889633278599,2.5593869731800765,4.836070060207991,0.0002736726874657909
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,1,356.7529300331321,142.61278850676808,7.001904189166896,2.471817431949409,4.9909265878471265,0.00027495188342040145
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,8,1318.381268005084,67.99149345226458,14.658002292964245,19.241061130334487,2.771224371931851,0.0017301038062283738
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,8,1329.4060683079044,67.16831898838693,14.848295789842382,19.780502043199068,2.8975912408759124,0.0
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,8,1377.751730141726,67.48168948759388,14.773663782103428,20.32423009877978,2.7414294015107497,0.0005810575246949448
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,16,1898.1974695419767,54.54229797321902,18.242018254493168,34.69590222861251,1.974584290928755,0.0014378145219266715
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,16,1920.3761411855278,54.19083803488631,18.36992395586107,35.28654124457308,1.9695263181797538,0.001447178002894356
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,16,1902.8094764025323,53.83679376781626,18.494520887836856,35.36270939548434,1.9661372763782403,0.0014566642388929353
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,32,2493.3092191921237,33.84463430158959,29.16548154960981,73.51059085841695,2.85741723704428,0.020066889632107024
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,32,2441.273043318879,45.562462252199836,21.665197506024096,53.341652323580035,1.1761443187937535,0.4406196213425129
+frank2,switched,"0,1,2,3",4,clustered_full,0.6,32,2521.374133575628,33.995558534184475,29.13023321568627,74.04152249134948,2.953865606936416,0.012687427912341407
+frank2,switched,"4,5,6,7",4,ordinary,0.8,1,353.3684717316786,147.94393510607196,6.7483164335628665,2.371588880387656,3.993369038510584,0.0
+frank2,switched,"4,5,6,7",4,ordinary,0.8,1,357.1974902975374,148.6475967479651,6.717534184911633,2.42785544711158,3.8815615932471643,0.0002637826431020839
+frank2,switched,"4,5,6,7",4,ordinary,0.8,1,361.92237439061836,149.12280101105796,6.694802772285038,2.4464896134630556,3.820404943465685,0.00026295030239284776
+frank2,switched,"4,5,6,7",4,ordinary,0.8,8,1351.8127032077157,71.2076217600095,14.00927534856828,18.922356828193834,2.4106060606060606,0.0005506607929515419
+frank2,switched,"4,5,6,7",4,ordinary,0.8,8,1327.9896253869897,69.95184772651905,14.247060341367712,18.94506726457399,2.5454800673778775,0.0005605381165919282
+frank2,switched,"4,5,6,7",4,ordinary,0.8,8,1344.3794579732662,70.81492193171971,14.07888315227021,19.028239202657808,2.3875086745315754,0.0011074197120708748
+frank2,switched,"4,5,6,7",4,ordinary,0.8,16,1907.5090370541484,56.18882127811277,17.689566297976274,33.80600139567341,1.7467708151509862,0.011863224005582694
+frank2,switched,"4,5,6,7",4,ordinary,0.8,16,1905.7172137894697,54.34592275641473,18.311492882395385,34.96536796536797,1.9268942472681296,0.0036075036075036075
+frank2,switched,"4,5,6,7",4,ordinary,0.8,16,1920.8958814693442,54.61940069118729,18.180487367552047,34.91744436468055,1.9026481149012566,0.021536252692031587
+frank2,switched,"4,5,6,7",4,ordinary,0.8,32,2490.2904785268634,44.778215043263685,22.085919969352016,55.62346760070052,1.3992584454820105,0.4115586690017513
+frank2,switched,"4,5,6,7",4,ordinary,0.8,32,2436.2789229953883,42.582329615894885,23.02654446593002,57.48710865561694,1.6610267534345626,0.3821362799263352
+frank2,switched,"4,5,6,7",4,ordinary,0.8,32,2312.929862244941,62.81542581285265,15.868003051186017,36.72846441947566,0.1925063588338877,0.813358302122347
diff --git a/docs/model-preparation.md b/docs/model-preparation.md
index 11039f3b..32147074 100644
--- a/docs/model-preparation.md
+++ b/docs/model-preparation.md
@@ -104,10 +104,10 @@ patches before starting their process:
 uv run --locked --extra prepare python -m ignition.patches
 ```
 
-`python scripts/setup_preparation.py` updates FlashInfer to the tip of its `main`
-branch, syncs preparation/test dependencies, rebuilds FlashInfer and Heliopause,
-and applies the bundled patches before exiting. It updates `uv.lock` when the
-branch advances.
+`python scripts/setup_preparation.py` resolves the FlashInfer revision declared
+in `pyproject.toml`, syncs preparation/test dependencies, rebuilds FlashInfer and
+Heliopause, and applies the bundled patches before exiting. It updates `uv.lock`
+when the declared dependency resolves differently.
 
 Build dependencies include a C/C++ compiler, CMake, Ninja, pkg-config, Protobuf's
 `protoc`, OpenSSL headers, Python headers, and liburing headers. On Ubuntu:
@@ -140,10 +140,10 @@ together. Refresh and commit `helios/Cargo.lock`, `flare/Cargo.lock`, and
 and Heliopause against the pinned revision. Compare rendered prompts and token
 IDs with upstream when changing encoder behavior.
 
-The preparation dependency tracks the `main` branch of
-`local-inference-lab/flashinfer`; `uv.lock` pins the resolved commit. The setup
-script updates it automatically. To update the lock alone, run
-`uv lock --upgrade-package flashinfer-python`, then sync the environment.
+The preparation dependency pins a revision of `local-inference-lab/flashinfer`
+in `pyproject.toml`; `uv.lock` records the same commit. To update FlashInfer,
+change that revision and run `uv lock --upgrade-package flashinfer-python`,
+then sync the environment.
 For local FlashInfer development, install the sibling
 checkout explicitly with `uv pip install --python .venv/bin/python --no-deps -e ../flashinfer`;
 `uv sync --locked --extra prepare --extra test` restores the Git dependency.
diff --git a/ignition/components/collectives.py b/ignition/components/collectives.py
index 69040b18..867905a7 100644
--- a/ignition/components/collectives.py
+++ b/ignition/components/collectives.py
@@ -143,10 +143,15 @@ def prepare(state):
                              inplace=source.data_ptr() == output.data_ptr())
                 self.runtime_profiles = tuple((mode, active) for mode, counts in row["profiles"].items() for active in counts)
                 self.runtime_cache_policy = ladder["cache_policy"]
-                # One active CTA is legal at every payload; runtime selects from the supplied profiles.
+                cluster_size = row.get("cluster_size", 1)
+                mode, active = self.runtime_profiles[0]
+                # Phased modes need a complete cluster, so use a declared clustered profile.
+                initial_active = active if cluster_size > 1 else 1
                 plan_options = dict(joint_only=True, override=CollectiveConfig(
                     implementation=row["implementation"], threads=row["threads"], blocks=row["blocks"],
-                    submode=self.runtime_profiles[0][0], active_blocks=1))
+                    submode=mode, active_blocks=initial_active))
+                if cluster_size > 1:
+                    plan_options["joint_cluster_size"] = cluster_size
             self.plan = runtime.plan(source, out=output, **plan_options)
             group = runtime.exchange_group
 
diff --git a/ignition/preparation/collective_ladder.py b/ignition/preparation/collective_ladder.py
index 4f7b838c..6f564cfe 100644
--- a/ignition/preparation/collective_ladder.py
+++ b/ignition/preparation/collective_ladder.py
@@ -12,6 +12,10 @@
     "joint_latency": ("full", "payload_rsag", "pull"),
     "joint_bandwidth": ("payload_rsag", "bulk_push", "bulk_pull", "pull"),
 }
+CLUSTER_MODES = {
+    "joint_latency": ("full_phased", "payload_rsag_phased"),
+    "joint_bandwidth": ("payload_rsag_phased",),
+}
 GATHER_MODES = {
     "gather_joint": ("barrier", "word_exchange"),
     "gather_selected_barrier": ("barrier",),
@@ -89,7 +93,8 @@ def require(condition, message):
     require(isinstance(data["buckets"], list) and bool(data["buckets"]), "buckets must be nonempty")
     previous = data["min_bytes"] - 16
     for row in data["buckets"]:
-        require(isinstance(row, dict) and set(row) == {"max_bytes", "implementation", "threads", "blocks", "profiles"},
+        required = {"max_bytes", "implementation", "threads", "blocks", "profiles"}
+        require(isinstance(row, dict) and required <= set(row) <= required | {"cluster_size"},
                 "invalid bucket fields")
         upper = row["max_bytes"]
         require(type(upper) is int and upper > previous and upper % 16 == 0,
@@ -98,12 +103,23 @@ def require(condition, message):
         require(isinstance(row["implementation"], str) and row["implementation"] in MODES, "unknown joint kernel")
         require(type(row["threads"]) is int and row["threads"] in (32, 64, 128, 256, 512), "invalid threads")
         require(type(row["blocks"]) is int and 1 <= row["blocks"] <= 64, "invalid physical grid")
+        cluster_size = row.get("cluster_size", 1)
+        require(type(cluster_size) is int and cluster_size in (1, 8), "cluster_size must be 1 or 8")
+        if cluster_size == 8:
+            require(tuple(data["compute_capability"]) in ((12, 0), (12, 1)),
+                    "clustered joint reductions require SM120 or SM121")
+            require(row["threads"] == 512 and row["blocks"] == 64,
+                    "clustered joint reductions require 512 threads and 64 physical CTAs")
         require(isinstance(row["profiles"], dict) and bool(row["profiles"]), "profiles must be nonempty")
         for mode, counts in row["profiles"].items():
-            require(mode in MODES[row["implementation"]], "mode does not belong to joint kernel")
+            phased = mode in CLUSTER_MODES[row["implementation"]]
+            require(mode in MODES[row["implementation"]] or (cluster_size == 8 and phased),
+                    "mode does not belong to the prepared joint kernel")
             require(isinstance(counts, list) and bool(counts)
                     and all(type(v) is int and 1 <= v <= row["blocks"] for v in counts), "invalid active CTA counts")
             require(len(set(counts)) == len(counts), "duplicate active CTA counts")
+            if phased:
+                require(counts == [cluster_size], "phased profiles require exactly one complete active cluster")
     _validate_gathers(data["all_gather"], data["world_size"])
     return data
 
diff --git a/ignition/preparation/ladders/README.md b/ignition/preparation/ladders/README.md
index 5b421ed7..58578830 100644
--- a/ignition/preparation/ladders/README.md
+++ b/ignition/preparation/ladders/README.md
@@ -77,6 +77,55 @@ validate exact payload bytes and select among the supplied profiles. Gathers
 with equal byte sizes use the same recommendations regardless of row width.
 RoCE gathers and reduction-only ladders retain their preparation policy.
 
+### Thread-block clusters
+
+A bucket may set `"cluster_size": 8` to launch eight-CTA CUDA thread-block
+clusters. Omitting the field, or setting it to `1`, retains an ordinary launch.
+Clustered buckets require SM120 or SM121 with at least 64 SMs, 512 threads per
+CTA, and 64 physical CTAs. Preparation checks that the compiled function can
+keep eight clusters resident. Ordinary profiles retain their active-CTA counts.
+Phased full all-reduce is named `full_phased`; phased reduce-scatter followed
+by all-gather is named `payload_rsag_phased`. The latency family admits both;
+the bandwidth family admits `payload_rsag_phased`. Each phased profile must select
+exactly eight active CTAs.
+
+Phased execution uses the first complete cluster to write one peer destination
+at a time. All its CTAs participate in each phase barrier; other clusters return
+without changing collective state. System-scope publication fences remain
+required for peer-visible writes. This uses existing kernel launch attributes
+in execution ABI 5 and does not require cooperative launch.
+
+`gb202_pcie_tp4_clustered.json` adds phased RSAG to 36 compatible buckets used
+by packed DeepSeek V4.1 TP4 verification graphs with eight, sixteen, or thirty-two
+requests, seven draft positions, and up to 128 target rows. It retains every
+ordinary candidate, kernel family, thread count, and physical grid from
+`gb202_pcie_tp4.json`. Its 256-thread buckets use ordinary launches.
+`gb202_pcie_tp4_clustered_full.json` adds one further choice: phased Full in the
+bucket containing 80 KiB. This also covers the target reductions for one request
+verified at eight rows.
+
+The compiled cluster geometry also applies when startup selects an ordinary
+profile; startup selection cannot disable clustering. Cluster scheduling can
+affect those profiles, so retaining every ordinary candidate does not guarantee
+unchanged latency. Qualify these catalogs on the intended GPU topology and
+complete serving workload; component improvements do not establish a throughput gain.
+`gb202_pcie_tp4.json` remains the ordinary catalog.
+
+Select the catalog with both phased modes during export:
+
+```sh
+.venv/bin/ignition export deepseek-v4.1-flash --gpus 0,1,2,3 \
+  --collective-tuning runtime \
+  --collective-ladder ignition/preparation/ladders/gb202_pcie_tp4_clustered_full.json
+```
+
+Use `gb202_pcie_tp4_clustered.json` in the same flag to exclude the phased Full
+choice at 80 KiB. `--collective-tuning static` uses preparation-time autotuning
+and accepts no ladder; these catalogs require the explicit `runtime` policy.
+Changing the catalog requires package export because cluster geometry is compiled
+into its kernels and captured launch attributes. Existing packages retain their
+prepared geometry and profiles.
+
 ## GB202 PCIe TP4 recommendations
 
 `gb202_pcie_tp4.json` contains 210 buckets from 16 bytes through 8 MiB.
diff --git a/ignition/preparation/ladders/gb202_pcie_tp4_clustered.json b/ignition/preparation/ladders/gb202_pcie_tp4_clustered.json
new file mode 100644
index 00000000..3f43eabe
--- /dev/null
+++ b/ignition/preparation/ladders/gb202_pcie_tp4_clustered.json
@@ -0,0 +1,437 @@
+{
+  "version": 2,
+  "name": "gb202_pcie_tp4_clustered",
+  "compute_capability": [
+    12,
+    0
+  ],
+  "world_size": 4,
+  "dtype": "bfloat16",
+  "inplace": false,
+  "cache_policy": "cold",
+  "min_bytes": 16,
+  "buckets": [
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diff --git a/ignition/preparation/ladders/gb202_pcie_tp4_clustered_full.json b/ignition/preparation/ladders/gb202_pcie_tp4_clustered_full.json
new file mode 100644
index 00000000..bf1e9acb
--- /dev/null
+++ b/ignition/preparation/ladders/gb202_pcie_tp4_clustered_full.json
@@ -0,0 +1,437 @@
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+      {"max_bytes": 8388608,"implementation": "gather_selected_barrier","threads": 512,"blocks": 64,"profiles": {"barrier": [1,2,64]}}
+    ]
+  }
+}
diff --git a/pyproject.toml b/pyproject.toml
index 95ccbb94..4ef2aa40 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,7 +22,7 @@ prepare = [
     "torch==2.14.1+cu132",
     "torchvision==0.29.1+cu132",
     "transformers[torch,vision,chat_template] @ git+https://github.com/huggingface/transformers.git@1d9654e78a23fb167ef539cbd699dfbd3e4830cf",
-    "flashinfer-python[b12x] @ git+https://github.com/local-inference-lab/flashinfer.git@main",
+    "flashinfer-python[b12x] @ git+https://github.com/local-inference-lab/flashinfer.git@560fef16648e4953bf39b701a226c322a0ab6c7c",
     "cuda-python==13.3.1",
     "cuda-tile[tileiras]==1.6.0",
     "safetensors==0.8.0",
diff --git a/tests/components/test_clustered_gather_interleaving_cuda.py b/tests/components/test_clustered_gather_interleaving_cuda.py
new file mode 100644
index 00000000..a7e28471
--- /dev/null
+++ b/tests/components/test_clustered_gather_interleaving_cuda.py
@@ -0,0 +1,118 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Clustered reductions feed device-selected gathers across captured row sizes."""
+
+from datetime import timedelta
+import os
+from pathlib import Path
+
+import pytest
+
+pytestmark = pytest.mark.skipif(os.environ.get("FLARE_RUN_TP_CUDA_TESTS") != "1",
+                               reason="requires four explicitly assigned GPUs")
+
+
+def _rank(rank, directory):
+    import torch
+    import torch.distributed as dist
+    from ignition.components.collectives import PreparedAllGather, PreparedAllReduce, _all_gather, _all_reduce
+    from ignition.preparation.aot import StorageBackend
+    from ignition.preparation.collective_ladder import load
+    from ignition.preparation.collectives import (
+        CollectivePreparation, create_all_gather_runtime, create_all_reduce_runtime,
+    )
+    from ignition.preparation.startup_tuning import capture_consumers
+    from ignition.preparation.tuning import RecordedPreparationSession
+
+    torch.cuda.set_device(rank)
+    torch.set_num_threads(1)
+    device = torch.device("cuda", rank)
+    dist.init_process_group("gloo", init_method=(Path(directory) / "group").as_uri(),
+                            rank=rank, world_size=4, timeout=timedelta(minutes=3))
+    coordinator = CollectivePreparation(dist.group.WORLD)
+    reduction = create_all_reduce_runtime(dist.group.WORLD, device, max_tokens=32,
+                                         hidden_size=5120, collective_tuning="runtime")
+    gather = create_all_gather_runtime(dist.group.WORLD, device, max_rows=32, columns=4 * 5120)
+    session = RecordedPreparationSession(device=device, checkpoint="collective-dependencies",
+        workload_schema="reduce-then-gather", numerical_policy="exact", coordinator=coordinator,
+        collective_tuning="runtime", compile_workers=4, autotune_effort="default",
+        collective_ladder=load(Path(__file__).resolve().parents[2] /
+            "ignition/preparation/ladders/gb202_pcie_tp4_clustered_full.json"))
+    cases, graphs = {}, {}
+    try:
+        for rows in (8, 16, 32):
+            source = torch.zeros((rows, 5120), dtype=torch.bfloat16, device=device)
+            reduced = torch.empty_like(source)
+            gathered = torch.empty((rows, 4 * 5120), dtype=source.dtype, device=device)
+            r = PreparedAllReduce(source, reduced, runtime=reduction, session=session,
+                                  coordinator=coordinator, name=f"reduce-{rows}")
+            g = PreparedAllGather(reduced, gathered, runtime=gather, session=session,
+                                  coordinator=coordinator, name=f"gather-{rows}")
+            cases[rows] = source, reduced, gathered, r, g
+        session.tune(show_progress=False)
+
+        def forward(reduce_handle, gather_handle):
+            def run(source, reduced, gathered):
+                _all_reduce(source, reduced, reduce_handle)
+                _all_gather(reduced, gathered, gather_handle)
+            return run
+
+        for rows, (source, reduced, gathered, r, g) in cases.items():
+            assert r.plan.prepared.state.query.call["joint_cluster_size"] == 8
+            compiled = StorageBackend(forward(r.handle, g.handle), (source, reduced, gathered))
+            compiled(source, reduced, gathered)
+            torch.cuda.synchronize()
+            dist.barrier()
+            graph = torch.cuda.CUDAGraph()
+            with session.capture(), capture_consumers(session, graph), torch.cuda.graph(graph):
+                compiled(source, reduced, gathered)
+            assert session._startup_collectives.captures[id(graph)] == {id(r), id(g)}
+            graphs[rows] = graph, compiled
+        session.freeze()
+        assert {p.gather for p in session._startup_collectives.groups.values()} == {False, True}
+        for producer in session._startup_collectives.groups.values():
+            graph = torch.cuda.CUDAGraph()
+            with session.capture(), torch.cuda.graph(graph):
+                producer.run()
+            graph.replay()
+            torch.cuda.synchronize()
+            assert producer.status.item() == 0
+            choices = [None] * 4
+            dist.all_gather_object(choices, tuple(int(s.selector.item()) for s in producer.specs))
+            assert all(choice == choices[0] for choice in choices)
+            graph.reset()
+
+        for step, rows in enumerate((8, 32, 16, 8, 16, 32) * 3):
+            source, reduced, gathered, _, _ = cases[rows]
+            values = torch.arange(rows * 5120).reshape(1, rows, 5120)
+            operands = ((values + 3 * torch.arange(4).reshape(4, 1, 1) + step) % 16).to(torch.bfloat16)
+            expected = operands.float().sum(0).to(torch.bfloat16)
+            source.copy_(operands[rank])
+            reduced.fill_(float("nan"))
+            gathered.fill_(float("nan"))
+            before = torch.cuda.memory_stats()
+            graphs[rows][0].replay()
+            torch.cuda.synchronize()
+            after = torch.cuda.memory_stats()
+            for key in ("allocation.all.allocated", "allocation.all.freed", "segment.all.allocated"):
+                assert before[key] == after[key], key
+            assert torch.equal(reduced.cpu(), expected)
+            assert torch.equal(gathered.cpu(), expected.repeat(1, 4))
+            assert torch.equal(source.cpu(), operands[rank])
+    finally:
+        torch.cuda.synchronize()
+        for graph, _ in graphs.values():
+            graph.reset()
+        session.close()
+        gather.close()
+        reduction.close()
+        dist.destroy_process_group()
+
+
+def test_clustered_reduction_and_gather_share_captured_dependencies(tmp_path):
+    import torch
+    import torch.multiprocessing as mp
+
+    assert torch.cuda.device_count() == 4
+    mp.spawn(_rank, args=(str(tmp_path),), nprocs=4, join=True)
diff --git a/tests/components/test_collective_plan_choice.py b/tests/components/test_collective_plan_choice.py
index 1e85238d..f4846e99 100644
--- a/tests/components/test_collective_plan_choice.py
+++ b/tests/components/test_collective_plan_choice.py
@@ -112,6 +112,38 @@ def channel():
             session=object(), coordinator=object(), name="reduce")
 
 
+@pytest.mark.parametrize("first_profile", [("payload_rsag_phased", 8), ("payload_rsag", 64)])
+def test_clustered_ladder_prepares_a_valid_initial_profile(monkeypatch, first_profile):
+    from copy import deepcopy
+    from b12x.comm.pcie import CollectiveConfig
+    from ignition.components import collectives
+    from ignition.preparation import startup_tuning
+    from ignition.preparation.collective_ladder import load, validate
+
+    data = deepcopy(load("ignition/preparation/ladders/gb202_pcie_tp4.json"))
+    mode, active = first_profile
+    data["buckets"][0].update(threads=512, blocks=64, cluster_size=8, profiles={mode: [active]})
+    validate(data)
+    calls = []
+    plan = SimpleNamespace(request=lambda **kwargs: object())
+    runtime = SimpleNamespace(algorithm="oneshot", world_size=4, single_channel=True,
+        exchange_group=object(), for_stream=lambda: SimpleNamespace(_joint_workspace=object()),
+        plan=lambda *args, **kwargs: calls.append(kwargs) or plan)
+    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device: (12, 0))
+    monkeypatch.setattr(collectives, "_roce", lambda _: False)
+    monkeypatch.setattr(collectives, "declare", lambda *args, **kwargs: None)
+    monkeypatch.setattr(torch.distributed, "get_process_group_ranks", lambda _: [0, 1, 2, 3])
+    monkeypatch.setattr(startup_tuning, "register_collective", lambda *args: None)
+    source = torch.ones(8, dtype=torch.bfloat16)
+    output = torch.empty_like(source)
+    collectives.PreparedAllReduce(source, output, runtime=runtime,
+        session=SimpleNamespace(collective_tuning="runtime", collective_ladder=data),
+        coordinator=object(), name="hidden.reduce")
+    assert calls == [dict(out=output, joint_only=True, joint_cluster_size=8,
+                         override=CollectiveConfig(implementation="joint_latency", threads=512,
+                             blocks=64, submode=mode, active_blocks=active))]
+
+
 def test_collective_choice_keys_distinguish_alternatives_and_finalist_policy():
     from b12x.comm.pcie import joint_finalist_policy
     from b12x.comm.pcie._collective_tuning import CollectiveConfig, CollectiveQuery, TUNING
diff --git a/tests/preparation/test_collective_ladder.py b/tests/preparation/test_collective_ladder.py
index f91dae1e..f7f3500b 100644
--- a/tests/preparation/test_collective_ladder.py
+++ b/tests/preparation/test_collective_ladder.py
@@ -67,6 +67,90 @@ def test_invalid_bucket(changes):
         validate(data)
 
 
+def clustered_ladder():
+    data = deepcopy(load(LADDER))
+    data["buckets"][0].update(threads=512, blocks=64, cluster_size=8,
+                              profiles={"payload_rsag": [64], "pull": [2, 8], "payload_rsag_phased": [8]})
+    return data
+
+
+def test_clustered_geometry_retains_ordinary_profiles_and_exact_contents():
+    data = clustered_ladder()
+    expected = deepcopy(data)
+    assert validate(data) is data and data == expected
+    assert choose(data, 16) is data["buckets"][0]
+    ordinary = deepcopy(load(LADDER))
+    ordinary["buckets"][0]["cluster_size"] = 1
+    assert validate(ordinary) is ordinary
+
+
+@pytest.mark.parametrize("filename, changed_count", [
+    ("gb202_pcie_tp4_clustered.json", 36),
+    ("gb202_pcie_tp4_clustered_full.json", 37),
+])
+def test_clustered_catalog_preserves_every_ordinary_candidate(filename, changed_count):
+    baseline = load(LADDER)
+    clustered = load(LADDER.with_name(filename))
+    assert {key: value for key, value in clustered.items() if key not in ("name", "buckets")} == {
+        key: value for key, value in baseline.items() if key not in ("name", "buckets")}
+    assert len(clustered["buckets"]) == len(baseline["buckets"])
+    changed = []
+    for ordinary, candidate in zip(baseline["buckets"], clustered["buckets"], strict=True):
+        if candidate == ordinary:
+            continue
+        changed.append(candidate["max_bytes"])
+        without_cluster = deepcopy(candidate)
+        assert without_cluster.pop("cluster_size") == 8
+        for mode in ("payload_rsag_phased", "full_phased"):
+            if mode in without_cluster["profiles"]:
+                assert without_cluster["profiles"].pop(mode) == [8]
+        assert without_cluster == ordinary
+        assert ordinary["threads"] == 512 and ordinary["blocks"] == 64
+    assert len(changed) == changed_count
+    assert {169984, 339952} <= set(changed)
+    assert (84992 in changed) == filename.endswith("_full.json")
+
+
+def test_full_phase_catalog_differs_only_at_the_80_kib_bucket():
+    packed = load(LADDER.with_name("gb202_pcie_tp4_clustered.json"))
+    full = load(LADDER.with_name("gb202_pcie_tp4_clustered_full.json"))
+    changed = [(before, after) for before, after in zip(packed["buckets"], full["buckets"], strict=True)
+               if before != after]
+    assert len(changed) == 1
+    before, after = changed[0]
+    assert before["max_bytes"] == 84992
+    expected = deepcopy(before)
+    expected.update(cluster_size=8)
+    expected["profiles"]["full_phased"] = [8]
+    assert after == expected
+
+
+@pytest.mark.parametrize("changes", [dict(cluster_size=True), dict(cluster_size=0),
+    dict(cluster_size=16), dict(threads=256), dict(blocks=16),
+    dict(profiles={"payload_rsag_phased": [1]}), dict(profiles={"payload_rsag_phased": [16]}),
+    dict(profiles={"payload_rsag_phased": [8, 16]})])
+def test_clustered_profiles_reject_incomplete_or_multiple_active_clusters(changes):
+    data = clustered_ladder()
+    data["buckets"][0].update(changes)
+    with pytest.raises(UsageError, match="collective ladder"):
+        validate(data)
+
+
+def test_phased_profiles_require_clustered_supported_geometry():
+    data = clustered_ladder()
+    del data["buckets"][0]["cluster_size"]
+    with pytest.raises(UsageError, match="prepared joint kernel"):
+        validate(data)
+    data = clustered_ladder()
+    data["compute_capability"] = [10, 3]
+    with pytest.raises(UsageError, match="SM120 or SM121"):
+        validate(data)
+    data = clustered_ladder()
+    data["buckets"][0].update(implementation="joint_bandwidth", profiles={"full_phased": [8]})
+    with pytest.raises(UsageError, match="prepared joint kernel"):
+        validate(data)
+
+
 def test_runtime_requires_file_and_static_rejects_it(tmp_path):
     with pytest.raises(UsageError, match="requires --collective-ladder"):
         resolve("runtime", None)
@@ -119,13 +203,45 @@ def test_b12x_accepts_pinned_geometry_at_both_ends_of_every_bucket(effort):
             lower = row["max_bytes"] + 16
 
 
-def test_native_export_preserves_ladder_contents():
+@pytest.mark.parametrize("effort", ["off", "default", "audit"])
+@pytest.mark.parametrize("filename", ["gb202_pcie_tp4_clustered.json", "gb202_pcie_tp4_clustered_full.json"])
+def test_clustered_catalog_profiles_are_admitted_by_b12x(effort, filename):
+    from b12x.comm.pcie._collective_tuning import CollectiveConfig, CollectiveQuery, TUNING
+    from b12x.comm.pcie._joint_profile import selected_profile
+    from b12x.preparation import FrozenMapping, autotune_effort
+
+    data = load(LADDER.with_name(filename))
+    lower = data["min_bytes"]
+    with autotune_effort(effort):
+        for row in data["buckets"]:
+            if row.get("cluster_size", 1) == 8:
+                family = row["implementation"]
+                for mode, counts in row["profiles"].items():
+                    for active in counts:
+                        config = CollectiveConfig(implementation=family, threads=512, blocks=64,
+                                                  submode=mode, active_blocks=active)
+                        for size in (lower, row["max_bytes"]):
+                            query = CollectiveQuery(operation="all_reduce", world_size=4, rank=0,
+                                alternatives=FrozenMapping({family: dict(surface="OneshotAllReduce.all_reduce",
+                                    world_size=4, rank=0, topology="pcie_ipc", setup={"joint_max_blocks": 64},
+                                    call=dict(transport=family, threads=512, blocks=64, submode=mode, active_blocks=active,
+                                        size_packs=size // 16, stage_input=True, dtype="bfloat16",
+                                        joint_cluster_size=8))}), default=config)
+                            configured = TUNING.configure(query, device=None, override=config, search=effort != "off")
+                            assert configured.pinned == config
+                        encoded = selected_profile(family, 64, mode, active, cluster_size=8)
+                        assert encoded & 0xffff == active
+            lower = row["max_bytes"] + 16
+
+
+@pytest.mark.parametrize("clustered", [False, True])
+def test_native_export_preserves_ladder_contents(clustered):
     import json
     from ignition.preparation.package_proto import serving
     from ignition.preparation.runtime_package_pb2 import ModelIndex
     from tests.preparation.test_frontend_metadata import server
 
-    data = load(LADDER)
+    data = clustered_ladder() if clustered else load(LADDER)
     source = server()
     source["rebuild_manifest"] = dict(version=1, collective_tuning="runtime",
                                       collective_ladder_json=json.dumps(data))
diff --git a/uv.lock b/uv.lock
index ded918c4..4d7befe8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -565,7 +565,7 @@ requires-dist = [
     { name = "cuda-python", marker = "extra == 'prepare'", specifier = "==13.3.1" },
     { name = "cuda-tile", extras = ["tileiras"], marker = "extra == 'prepare'", specifier = "==1.6.0" },
     { name = "flare-heliopause", marker = "extra == 'prepare'", directory = "heliopause" },
-    { name = "flashinfer-python", extras = ["b12x"], marker = "extra == 'prepare'", git = "https://github.com/local-inference-lab/flashinfer.git?rev=main" },
+    { name = "flashinfer-python", extras = ["b12x"], marker = "extra == 'prepare'", git = "https://github.com/local-inference-lab/flashinfer.git?rev=560fef16648e4953bf39b701a226c322a0ab6c7c" },
     { name = "huggingface-hub", marker = "extra == 'prepare'", specifier = "==1.33.0" },
     { name = "openai", marker = "extra == 'api-test'", specifier = ">=2,<3" },
     { name = "pillow", marker = "extra == 'prepare'", specifier = "==12.3.0" },