diff --git a/MaxText/configs/a3/llama_2_70b/bf16/128vm.sh b/MaxText/configs/a3/llama_2_70b/bf16/128vm.sh
new file mode 100644
index 000000000..86356e29a
--- /dev/null
+++ b/MaxText/configs/a3/llama_2_70b/bf16/128vm.sh
@@ -0,0 +1,49 @@
+echo "Running 1vm.sh"
+
+# Example command to invoke this script via XPK on A3 or A3+:
+# For A3, you can set DEVICE_TYPE as `h100-80gb-8`.
+# For A3+, you can set DEVICE_TYPE as `h100-mega-80gb-8`.
+#
+# export RUN_NAME="llama2-70b-1vm-$(date +%Y-%m-%d-%H-%M)"
+# python3 xpk/xpk.py workload create --cluster ${CLUSTER_NAME} \
+# --workload ${WORKLOAD_NAME} --docker-image ${LOCAL_IMAGE_NAME} \
+# --device-type ${DEVICE_TYPE} --num-nodes 1 \
+# --command "bash MaxText/configs/a3/llama_2_70b/bf16/128vm.sh"
+
+# Stop execution if any command exits with error
+set -e
+
+export OUTPUT_PATH="gs://maxtext-experiments-multipod"
+
+# Set environment variables
+for ARGUMENT in "$@"; do
+    IFS='=' read -r KEY VALUE <<< "$ARGUMENT"
+    export "$KEY"="$VALUE"
+done
+
+# The setup accommodates two cases:
+# 1) Passing the 'RUN_NAME' variable at runtime
+# 2) Propagating the 'M_RUN_NAME' variable within an Airflow sweeping workflow
+if [ -n "$RUN_NAME" ];
+then
+    export M_RUN_NAME=$RUN_NAME
+fi
+
+export XLA_FLAGS="--xla_dump_to=$OUTPUT_PATH/$M_RUN_NAME/HLO_dumps/ --xla_dump_hlo_pass_re=.*
+ --xla_gpu_enable_latency_hiding_scheduler=true --xla_gpu_enable_address_computation_fusion=false
+ --xla_gpu_enable_triton_gemm=false --xla_gpu_graph_level=0 --xla_gpu_enable_highest_priority_async_stream=true
+ --xla_gpu_all_reduce_combine_threshold_bytes=536870912 --xla_gpu_all_gather_combine_threshold_bytes=2147483648
+ --xla_gpu_reduce_scatter_combine_threshold_bytes=33554432 --xla_gpu_enable_pipelined_all_gather=true
+ --xla_gpu_enable_pipelined_all_reduce=true --xla_gpu_enable_while_loop_double_buffering=true 
+ --xla_gpu_enable_triton_softmax_fusion=false --xla_gpu_enable_all_gather_combine_by_dim=false 
+ --xla_gpu_enable_reduce_scatter_combine_by_dim=false --xla_disable_hlo_passes=rematerialization
+ --xla_gpu_pgle_profile_file_or_directory_path=/app/MaxText/configs/a3/llama_2_70b/bf16/pgle/128vm.pbtxt"
+
+# 128 nodes
+python MaxText/train.py MaxText/configs/base.yml hardware=gpu base_output_directory=$OUTPUT_PATH \
+    steps=30 model_name=llama2-70b enable_checkpointing=false attention=cudnn_flash_te dataset_type=synthetic \
+    async_checkpointing=false profiler=xplane use_iota_embed=true scan_layers=true per_device_batch_size=4 \
+    remat_policy=save_qkv_proj logits_dot_in_fp32=false max_target_length=4096 \
+    ici_fsdp_parallelism=8 dcn_fsdp_parallelism=16 ici_data_parallelism=1 dcn_data_parallelism=8 \
+    ici_tensor_parallelism=1 dcn_tensor_parallelism=1
+
diff --git a/MaxText/configs/a3/llama_2_70b/bf16/256vm.sh b/MaxText/configs/a3/llama_2_70b/bf16/256vm.sh
new file mode 100644
index 000000000..45ae8ece5
--- /dev/null
+++ b/MaxText/configs/a3/llama_2_70b/bf16/256vm.sh
@@ -0,0 +1,49 @@
+echo "Running 1vm.sh"
+
+# Example command to invoke this script via XPK on A3 or A3+:
+# For A3, you can set DEVICE_TYPE as `h100-80gb-8`.
+# For A3+, you can set DEVICE_TYPE as `h100-mega-80gb-8`.
+#
+# export RUN_NAME="llama2-70b-1vm-$(date +%Y-%m-%d-%H-%M)"
+# python3 xpk/xpk.py workload create --cluster ${CLUSTER_NAME} \
+# --workload ${WORKLOAD_NAME} --docker-image ${LOCAL_IMAGE_NAME} \
+# --device-type ${DEVICE_TYPE} --num-nodes 1 \
+# --command "bash MaxText/configs/a3/llama_2_70b/bf16/256vm.sh"
+
+# Stop execution if any command exits with error
+set -e
+
+export OUTPUT_PATH="gs://maxtext-experiments-multipod"
+
+# Set environment variables
+for ARGUMENT in "$@"; do
+    IFS='=' read -r KEY VALUE <<< "$ARGUMENT"
+    export "$KEY"="$VALUE"
+done
+
+# The setup accommodates two cases:
+# 1) Passing the 'RUN_NAME' variable at runtime
+# 2) Propagating the 'M_RUN_NAME' variable within an Airflow sweeping workflow
+if [ -n "$RUN_NAME" ];
+then
+    export M_RUN_NAME=$RUN_NAME
+fi
+
+export XLA_FLAGS="--xla_dump_to=$OUTPUT_PATH/$M_RUN_NAME/HLO_dumps/ --xla_dump_hlo_pass_re=.*
+ --xla_gpu_enable_latency_hiding_scheduler=true --xla_gpu_enable_address_computation_fusion=false
+ --xla_gpu_enable_triton_gemm=false --xla_gpu_graph_level=0 --xla_gpu_enable_highest_priority_async_stream=true
+ --xla_gpu_all_reduce_combine_threshold_bytes=536870912 --xla_gpu_all_gather_combine_threshold_bytes=2147483648
+ --xla_gpu_reduce_scatter_combine_threshold_bytes=33554432 --xla_gpu_enable_pipelined_all_gather=true
+ --xla_gpu_enable_pipelined_all_reduce=true --xla_gpu_enable_while_loop_double_buffering=true 
+ --xla_gpu_enable_triton_softmax_fusion=false --xla_gpu_enable_all_gather_combine_by_dim=false 
+ --xla_gpu_enable_reduce_scatter_combine_by_dim=false --xla_disable_hlo_passes=rematerialization
+ --xla_gpu_pgle_profile_file_or_directory_path=/app/MaxText/configs/a3/llama_2_70b/bf16/pgle/256vm.pbtxt"
+
+# 256 nodes
+python MaxText/train.py MaxText/configs/base.yml hardware=gpu base_output_directory=$OUTPUT_PATH \
+    steps=30 model_name=llama2-70b enable_checkpointing=false attention=cudnn_flash_te dataset_type=synthetic \
+    async_checkpointing=false profiler=xplane use_iota_embed=true scan_layers=true per_device_batch_size=4 \
+    remat_policy=save_qkv_proj logits_dot_in_fp32=false max_target_length=4096 \
+    ici_fsdp_parallelism=8 dcn_fsdp_parallelism=16 ici_data_parallelism=1 dcn_data_parallelism=16 \
+    ici_tensor_parallelism=1 dcn_tensor_parallelism=1
+