diff --git a/benchmarks/geo_gnn/main.py b/benchmarks/geo_gnn/main.py
index 71e1c8827..b8875d2bf 100644
--- a/benchmarks/geo_gnn/main.py
+++ b/benchmarks/geo_gnn/main.py
@@ -78,14 +78,20 @@ def train_degree(train_dataset):
     # Compute the maximum in-degree in the training data.
     max_degree = -1
     for data in train_dataset:
-        d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
-        max_degree = max(max_degree, int(d.max()))
+        try:
+            d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
+            max_degree = max(max_degree, int(d.max()))
+        except TypeError:
+            pass
 
     # Compute the in-degree histogram tensor
     deg = torch.zeros(max_degree + 1, dtype=torch.long)
     for data in train_dataset:
-        d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
-        deg += torch.bincount(d, minlength=deg.numel())
+        try:
+            d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
+            deg += torch.bincount(d, minlength=deg.numel())
+        except TypeError:
+            pass
 
     return deg
 
@@ -109,13 +115,14 @@ def batch_size(x):
     observer = BenchObserver(batch_size_fn=batch_size)
 
     train_dataset = PCQM4Mv2Subset(args.num_samples, args.root)
+    degree = train_degree(train_dataset)
 
     sample = next(iter(train_dataset))
 
     info = models[args.model](
         args,
         sample=sample,
-        degree=lambda: train_degree(train_dataset),
+        degree=lambda: degree,
     )
 
     TRAIN_mean, TRAIN_std = (
diff --git a/config/base.yaml b/config/base.yaml
index 6e68dbdb6..c6a3540a9 100644
--- a/config/base.yaml
+++ b/config/base.yaml
@@ -733,10 +733,16 @@ _geo_gnn:
   plan:
     method: per_gpu
 
+pna:
+  inherits: _geo_gnn
+  argv:
+    --model: 'PNA'
+    --num-samples: 100000
+    --batch-size: 4096
+    --num-workers: "auto({n_worker}, 0)"
+
 dimenet:
   inherits: _geo_gnn
-  tags:
-    - monogpu
   argv:
     --model: 'DimeNet'
     --num-samples: 10000
@@ -744,7 +750,6 @@ dimenet:
     --batch-size: 16
     --num-workers: "auto({n_worker}, 0)"
 
-
 recursiongfn:
   inherits: _defaults
   definition: ../benchmarks/recursiongfn
diff --git a/config/scaling.yaml b/config/scaling.yaml
index f7bbbb282..b6226a37d 100644
--- a/config/scaling.yaml
+++ b/config/scaling.yaml
@@ -55,13 +55,13 @@ bert-tf32-fp16:
     112: 81140.75 MiB
   optimized: 128
 bf16: {}
-brax:
-  arg: --batch-size
-  model:
-    1024: 4912.25 MiB
-cleanrljax:
-  arg: --num_steps
-  optimized: 128
+# brax:
+#   arg: --batch-size
+#   model:
+#     1024: 4912.25 MiB
+# cleanrljax:
+#   arg: --num_steps
+#   optimized: 128
 convnext_large-fp16:
   arg: --batch-size
   model:
@@ -194,6 +194,9 @@ diffusion-single:
     4: 23478.75 MiB
     16: 33850.25 MiB
     32: 55354.25 MiB
+pna:
+  arg: --batch-size
+
 dimenet:
   arg: --batch-size
   model:
@@ -221,14 +224,14 @@ dinov2-giant-single:
     16: 52748.25 MiB
     32: 74544.25 MiB
 dlrm: {}
-dqn:
-  arg: --buffer_batch_size
-  model:
-    1024: 81.81005859375 MiB
-    2048: 83.40380859375 MiB
-    32768: 131.21630859375 MiB
-    65536: 182.21630859375 MiB
-  optimized: 128
+# dqn:
+#   arg: --buffer_batch_size
+#   model:
+#     1024: 81.81005859375 MiB
+#     2048: 83.40380859375 MiB
+#     32768: 131.21630859375 MiB
+#     65536: 182.21630859375 MiB
+#   optimized: 128
 focalnet:
   arg: --batch-size
   model:
@@ -338,18 +341,18 @@ opt-6_7b-multinode:
   model:
     1: 55380 MiB
   optimized: 1
-ppo:
-  arg: --num_steps
-  model:
-    8: 80.791748046875 MiB
-    16: 80.916748046875 MiB
-    32: 81.166748046875 MiB
-    64: 81.666748046875 MiB
-    128: 82.666748046875 MiB
-    1024: 96.666748046875 MiB
-    2048: 132.484619140625 MiB
-    4096: 205.328369140625 MiB
-  optimized: 32
+# ppo:
+#   arg: --num_steps
+#   model:
+#     8: 80.791748046875 MiB
+#     16: 80.916748046875 MiB
+#     32: 81.166748046875 MiB
+#     64: 81.666748046875 MiB
+#     128: 82.666748046875 MiB
+#     1024: 96.666748046875 MiB
+#     2048: 132.484619140625 MiB
+#     4096: 205.328369140625 MiB
+#   optimized: 32
 recursiongfn:
   arg: --batch_size
   model:
diff --git a/config/standard.yaml b/config/standard.yaml
index 084120f32..ac39481ad 100644
--- a/config/standard.yaml
+++ b/config/standard.yaml
@@ -162,7 +162,6 @@ ppo:
   enabled: true
   weight: 1.0
 
-
 cleanrljax:
   enabled: false
   weight: 1.0
@@ -172,6 +171,10 @@ dimenet:
   enabled: true
   weight: 1.0
 
+pna: 
+  enabled: False
+  weight: 1.0
+
 recursiongfn:
   enabled: true
   weight: 1.0
diff --git a/scripts/article/run_cuda.sh b/scripts/article/run_cuda.sh
index 03c535540..83c956d18 100644
--- a/scripts/article/run_cuda.sh
+++ b/scripts/article/run_cuda.sh
@@ -95,13 +95,15 @@ if [ "$MILABENCH_PREPARE" -eq 0 ]; then
     ARGS="--select resnet50-noio,brax,lightning,dinov2-giant-single,dinov2-giant-gpus,llm-lora-ddp-gpus,llm-lora-ddp-nodes,llm-lora-mp-gpus,llm-full-mp-gpus,llm-full-mp-nodes,dqn,ppo,dimenet,llava-single,rlhf-single,rlhf-gpus,vjepa-single,vjepa-gpus"
 
     MEMORY_CAPACITY=("4Go" "8Go" "16Go" "32Go" "64Go" "80Go")
-    
+    # MEMORY_CAPACITY=("2048" "4096" "8192")
+
     #   Run the benchmakrs 
     for CAPACITY in "${MEMORY_CAPACITY[@]}"; do
         export MILABENCH_SIZER_AUTO=1
         export MILABENCH_SIZER_MULTIPLE=8
-        export MILABENCH_SIZER_BATCH_SIZE=$CAPACITY
-        milabench run --run-name "c$CAPACITY.{time}" --system $MILABENCH_WORDIR/system.yaml $ARGS || true
+        export MILABENCH_SIZER_CAPACITY=$CAPACITY
+        # export MILABENCH_SIZER_BATCH_SIZE=$CAPACITY
+        milabench run --run-name "bs$CAPACITY.{time}" --system $MILABENCH_WORDIR/system.yaml $ARGS|| true
     done
 
     #