diff --git a/axlearn/experiments/text/gpt/fuji.py b/axlearn/experiments/text/gpt/fuji.py
index 69f6b1102..2f72fbac0 100644
--- a/axlearn/experiments/text/gpt/fuji.py
+++ b/axlearn/experiments/text/gpt/fuji.py
@@ -15,6 +15,7 @@
 import itertools
 from typing import Any, Optional, Union
 
+import jax
 from jax.ad_checkpoint import checkpoint_policies as jax_remat_policies
 
 from axlearn.common import causal_lm, config
@@ -22,6 +23,7 @@
     BaseStackedTransformerLayer,
     FusedGroupedQKVLinear,
     FusedQKVLinear,
+    GroupedQKVLinear,
     GroupedQueryAttention,
     MultiheadAttention,
     RepeatedTransformerLayer,
@@ -174,6 +176,12 @@ def get_trainer_kwargs(
             train_batch_size=train_batch_size,
             max_step=max_step,
             mesh_shape=mesh_shape_from_axes(data=-1, fsdp=8),
+            mesh_rules=(
+                (
+                    "neuron-(trn2|trn2n).48xlarge-64",
+                    mesh_shape_from_axes(fsdp=-1, model=4),
+                ),
+            )
         )
     elif model_size == "3B":
         trainer_kwargs = dict(
@@ -192,6 +200,12 @@ def get_trainer_kwargs(
             train_batch_size=train_batch_size,
             max_step=max_step,
             mesh_shape=mesh_shape_from_axes(data=-1, fsdp=8),
+            mesh_rules=(
+                (
+                    "neuron-(trn2|trn2n).48xlarge-64",
+                    mesh_shape_from_axes(fsdp=-1, model=4),
+                ),
+            )
         )
     elif model_size == "7B":
         trainer_kwargs = dict(
@@ -287,6 +301,14 @@ def get_trainer_kwargs(
                     "gpu-(p5.48xlarge|p4de.24xlarge|a3-highgpu-8g)-(256|512|1024)",
                     mesh_shape_from_axes(data=-1, fsdp=8),
                 ),
+                (
+                    "neuron-(trn2|trn2n).48xlarge-64",
+                    mesh_shape_from_axes(fsdp=-1, model=4),
+                ),
+                (
+                    "neuron-(trn1|trn1n).32xlarge-64",
+                    mesh_shape_from_axes(fsdp=-1, model=8),
+                ),
             ),
         )
     elif model_size == "8B":
@@ -367,6 +389,10 @@ def get_trainer_kwargs(
                     "gpu-(p5.48xlarge|p4de.24xlarge|a3-highgpu-8g)-(256|512|1024)",
                     mesh_shape_from_axes(data=-1, fsdp=8),
                 ),
+                (
+                    "neuron-(trn2|trn2n).48xlarge-64",
+                    mesh_shape_from_axes(fsdp=-1, model=4),
+                ),
             ),
         )
     elif model_size == "70B":
@@ -417,12 +443,17 @@ def get_trainer_kwargs(
                     "gpu-(p5.48xlarge|p4de.24xlarge)-(512|1024)",
                     mesh_shape_from_axes(data=-1, fsdp=128),
                 ),
+                (
+                    "neuron-(trn2|trn2n).48xlarge-64",
+                    mesh_shape_from_axes(fsdp=-1, model=4),
+                ),
             ),
         )
     else:
         raise NotImplementedError(f"Unknown model size {model_size}.")
     model_kwargs = trainer_kwargs.pop("model_kwargs")
     model_kwargs.setdefault("vocab_size", vocab_size)
+    model_kwargs.setdefault("stack_cfg", None if backend != "neuron" else StackedTransformerLayer.default_config())
     trainer_kwargs["model_cfg"] = model_config(**model_kwargs)
     trainer_kwargs["learner_cfg"] = adamw_decoupled_learner_config(
         max_step=trainer_kwargs["max_step"],
@@ -473,7 +504,9 @@ def model_config(
         ffn_dim = scaled_hidden_dim(scale=8 / 3, round_up_to_multiples_of=256)
     if num_kv_heads:
         atten_cfg = GroupedQueryAttention.default_config()
-        atten_input_linear = FusedGroupedQKVLinear.default_config().set(num_kv_heads=num_kv_heads)
+        backend = jax.default_backend()
+        qkv_linear =  FusedGroupedQKVLinear if backend != "neuron" else GroupedQKVLinear
+        atten_input_linear = qkv_linear.default_config().set(num_kv_heads=num_kv_heads)
     else:
         atten_cfg = MultiheadAttention.default_config()
         atten_input_linear = FusedQKVLinear.default_config()