AI-Hypercomputer · ankitkinra · Feb 21, 2026 · Feb 18, 2026
diff --git a/...eam-Maxtext/Llama-4-Maverick-17B-128E/prepare-model/templates/model-serve-downloader.yaml b/...eam-Maxtext/Llama-4-Maverick-17B-128E/prepare-model/templates/model-serve-downloader.yaml
@@ -95,7 +95,7 @@ spec:
               --model-size ${MODEL_SIZE} \
               --huggingface-checkpoint True 
 
-            gsutil -m cp -r ${CHECKPOINT_TPU_UNSCANNED} /gcs/{{ .Values.model.name }}/output/unscanned_ckpt/checkpoints/
+            gcloud storage cp --recursive ${CHECKPOINT_TPU_UNSCANNED} /gcs/{{ .Values.model.name }}/output/unscanned_ckpt/checkpoints/
 
             echo "Conversion Job Complete. Unscanned checkpoints should be at ${CHECKPOINT_TPU_UNSCANNED}"
 

diff --git a/...tStream-Maxtext/Llama-4-Scout-17B-16E/prepare-model/templates/model-serve-downloader.yaml b/...tStream-Maxtext/Llama-4-Scout-17B-16E/prepare-model/templates/model-serve-downloader.yaml
@@ -106,7 +106,7 @@ spec:
 
             echo "Conversion Job Complete. Unscanned checkpoints should be at ${CHECKPOINT_TPU_UNSCANNED}"
             echo "Copying unscanned checkpoints to GCS bucket..."
-            gsutil -m cp -r ${CHECKPOINT_TPU_UNSCANNED} gs://${GCS_FUSE_BUCKET}/{{ .Values.model.name }}/output/unscanned_ckpt/
+            gcloud storage cp --recursive ${CHECKPOINT_TPU_UNSCANNED} gs://${GCS_FUSE_BUCKET}/{{ .Values.model.name }}/output/unscanned_ckpt/
             echo "Finished copying unscanned checkpoints to gs://${GCS_FUSE_BUCKET}/{{ .Values.model.name }}/output/unscanned_ckpt/"
 
         volumeMounts:

diff --git a/inference/trillium/JetStream-Maxtext/Llama2-7B/README.md b/inference/trillium/JetStream-Maxtext/Llama2-7B/README.md
@@ -42,7 +42,7 @@ bash download.sh # When prompted, choose 7B. This should create a directory llam
 export CHKPT_BUCKET=gs://...
 export MAXTEXT_BUCKET_SCANNED=gs://...
 export MAXTEXT_BUCKET_UNSCANNED=gs://...
-gsutil cp -r llama/llama-2-7b/* ${CHKPT_BUCKET}
+gcloud storage cp --recursive llama/llama-2-7b/* ${CHKPT_BUCKET}
 
 
 # Checkpoint conversion
@@ -117,4 +117,4 @@ Mean TPOT: 5052.76 ms
 Median TPOT: 164.01 ms
 P99 TPOT: 112171.56 ms
 
-```
+```
diff --git a/inference/v5e/JetStream-Maxtext/Llama2-7B/README.md b/inference/v5e/JetStream-Maxtext/Llama2-7B/README.md
@@ -42,7 +42,7 @@ bash download.sh # When prompted, choose 7B. This should create a directory llam
 export CHKPT_BUCKET=gs://...
 export MAXTEXT_BUCKET_SCANNED=gs://...
 export MAXTEXT_BUCKET_UNSCANNED=gs://...
-gsutil cp -r llama/llama-2-7b ${CHKPT_BUCKET}
+gcloud storage cp --recursive llama/llama-2-7b ${CHKPT_BUCKET}
 
 
 # Checkpoint conversion
@@ -117,4 +117,4 @@ Mean TPOT: 5052.76 ms
 Median TPOT: 164.01 ms
 P99 TPOT: 112171.56 ms
 
-```
+```
diff --git a/microbenchmarks/trillium/collectives/README.md b/microbenchmarks/trillium/collectives/README.md
@@ -33,15 +33,15 @@ psum_ici: Matrix size: 17408x17408, dtype=<class 'jax.numpy.bfloat16'>, matrix_s
 
 Results will be printed out and also stored at `/tmp/microbenchmarks/collectives`. You can save the stored results to GCS by adding the following to `--command` in the XPK command:
 ```
-gsutil cp -r /tmp/microbenchmarks/collectives gs://<your-gcs-bucket>
+gcloud storage cp --recursive /tmp/microbenchmarks/collectives gs://<your-gcs-bucket>
 ```
 
 ### Run with a custom yaml config
 If you would like to run with a custom defined yaml with modified configurations (e.g. warmup_tries, tries, matrix_dim_range) you may do so by uploading it to a GCS bucket, pulling the yaml file from GCS in the workload, and then referencing the yaml file in the benchmark command. 
 
 Start by creating a yaml file `your_config.yaml`. Take a look at [1x_v6e_256.yaml](https://github.com/AI-Hypercomputer/accelerator-microbenchmarks/blob/35c10a42e8cfab7593157327dd3ad3150e4c001d/configs/1x_v6e_256.yaml) for an example yaml config. Then upload it to your GCS bucket:
 ```
-gsutil cp your_config.yaml gs://<your-gcs-bucket>
+gcloud storage cp your_config.yaml gs://<your-gcs-bucket>
 ```
 
 Then use a modified launch command that pulls the yaml file from GCS and references it in the benchmark command:
@@ -51,7 +51,7 @@ python3 ~/xpk/xpk.py workload create \
     --project=${PROJECT} \
     --zone=${ZONE} \
     --device-type=v6e-256 \
-    --command="git clone https://github.com/AI-Hypercomputer/accelerator-microbenchmarks.git && cd accelerator-microbenchmarks && git checkout trillium-collectives && pip install -r requirements.txt && echo '4096 41943040 314572800' > /proc/sys/net/ipv4/tcp_rmem && export LIBTPU_INIT_ARGS='--megascale_grpc_premap_memory_bytes=17179869184 --xla_tpu_enable_sunk_dcn_allreduce_done_with_host_reduction=true' && gsutil cp gs://<your-gcs-bucket>/your_config.yaml configs/ && python src/run_benchmark.py --config=configs/your_config.yaml" \
+    --command="git clone https://github.com/AI-Hypercomputer/accelerator-microbenchmarks.git && cd accelerator-microbenchmarks && git checkout trillium-collectives && pip install -r requirements.txt && echo '4096 41943040 314572800' > /proc/sys/net/ipv4/tcp_rmem && export LIBTPU_INIT_ARGS='--megascale_grpc_premap_memory_bytes=17179869184 --xla_tpu_enable_sunk_dcn_allreduce_done_with_host_reduction=true' && gcloud storage cp gs://<your-gcs-bucket>/your_config.yaml configs/ && python src/run_benchmark.py --config=configs/your_config.yaml" \
     --num-slices=1 \
     --docker-image=us-docker.pkg.dev/cloud-tpu-images/jax-stable-stack/tpu:jax0.5.2-rev1 \
     --workload=${WORKLOAD_NAME}

diff --git a/training/archive/trillium/Llama3.0-70B-PyTorch/XPK/README.md b/training/archive/trillium/Llama3.0-70B-PyTorch/XPK/README.md
@@ -1,5 +1,3 @@
-
-
 # Instructions for training Llama 3.0 70B on Trillium TPU on multipod using XPK
 
 ## Environment Steup
@@ -88,7 +86,7 @@ You can use the profile
 export PROFILE_SCRIPT_PATH=../../../../utils/
 
 # download the profile from gcp bucket to local
-gsutil cp -r $PROFILE_LOG_DIR ./
+gcloud storage cp --recursive $PROFILE_LOG_DIR ./
 
 # locate the xplane.pb file and process
 PYTHONPATH==$PROFILE_SCRIPT_PATH:$PYTHONPATH python $PROFILE_SCRIPT_PATH/profile_convert.py xplane.pb
@@ -112,4 +110,3 @@ Plane ID: 2, Name: /device:TPU:0
 Got 10 iterations
 1.8454
 ```
-
diff --git a/training/archive/trillium/Llama3.0-8B-PyTorch/XPK/README.md b/training/archive/trillium/Llama3.0-8B-PyTorch/XPK/README.md
@@ -1,5 +1,3 @@
-
-
 # Instructions for training Llama 3.0 8B on Trillium TPU on multipod using XPK
 
 ## Environment Steup
@@ -87,7 +85,7 @@ You can use the profile
 export PROFILE_SCRIPT_PATH=../../../../utils/
 
 # download the profile from gcp bucket to local
-gsutil cp -r $PROFILE_LOG_DIR ./
+gcloud storage cp --recursive $PROFILE_LOG_DIR ./
 
 # locate the xplane.pb file and process
 PYTHONPATH==$PROFILE_SCRIPT_PATH:$PYTHONPATH python $PROFILE_SCRIPT_PATH/profile_convert.py xplane.pb
@@ -111,4 +109,3 @@ Plane ID: 2, Name: /device:TPU:0
 Got 10 iterations
 1.8454
 ```
-
diff --git a/training/archive/trillium/Llama3.1-405B-PyTorch/XPK/README.md b/training/archive/trillium/Llama3.1-405B-PyTorch/XPK/README.md
@@ -99,7 +99,7 @@ You can use the profile
 export PROFILE_SCRIPT_PATH=../../../../utils/
 
 # download the profile from gcp bucket to local
-gsutil cp -r $PROFILE_LOG_DIR ./
+gcloud storage cp --recursive $PROFILE_LOG_DIR ./
 
 # locate the profile output ending with ".pb".
 # Name it xplane.pb file, and process it

diff --git a/training/archive/trillium/Mixtral-8x7B-Pytorch/XPK/README.md b/training/archive/trillium/Mixtral-8x7B-Pytorch/XPK/README.md
@@ -1,5 +1,3 @@
-
-
 # Instructions for training Mixtral 8x7B on Trillium TPU on multipod using XPK
 
 ## Environment Steup
@@ -87,7 +85,7 @@ You can use the profile
 # this is the place we place the profile processing script
 export PROFILE_SCRIPT_PATH=../../../../utils/
 # download the profile from gcp bucket to local
-gsutil cp -r $PROFILE_LOG_DIR ./
+gcloud storage cp --recursive $PROFILE_LOG_DIR ./
 # locate the xplane.pb file and process
 PYTHONPATH==$PROFILE_SCRIPT_PATH:$PYTHONPATH python $PROFILE_SCRIPT_PATH/profile_convert.py xplane.pb
 ```
@@ -110,4 +108,3 @@ Plane ID: 2, Name: /device:TPU:0
 Got 10 iterations
 1.8454
 ```
-
diff --git a/training/ironwood/wan2.1-14b/bf16-tpu7x-4x4x4/xpk/README.md b/training/ironwood/wan2.1-14b/bf16-tpu7x-4x4x4/xpk/README.md
@@ -243,7 +243,7 @@ huggingface-cli download RaphaelLiu/PusaV1_training --repo-type dataset --local-
 python src/maxdiffusion/data_preprocessing/wan_pusav1_to_tfrecords.py src/maxdiffusion/configs/base_wan_14b.yml train_data_dir=${HF_DATASET_DIR} tfrecords_dir=${TFRECORDS_DATASET_DIR} no_records_per_shard=10 skip_jax_distributed_system=True
 
 # Upload to gcs
-gsutil -m cp -r ${TFRECORDS_DATASET_DIR} ${DATASET_DIR}
+gcloud storage cp --recursive ${TFRECORDS_DATASET_DIR} ${DATASET_DIR}
 ```
 
 ## Run the recipe