diff --git a/bin/deterministic/model-size b/bin/deterministic/model-size
index 0d9a7df25..ab955065d 100755
--- a/bin/deterministic/model-size
+++ b/bin/deterministic/model-size
@@ -13,6 +13,7 @@ import yaml
 
 
 from ml_downscaling_emulator.deterministic.utils import create_model
+from ml_downscaling_emulator.utils import model_size, param_count
 
 
 logger = logging.getLogger()
@@ -49,10 +50,14 @@ def main(
     config_path = os.path.join(workdir, "config.yml")
     config = load_config(config_path)
     model = load_model(config)["model"]
-    num_score_model_parameters = sum(p.numel() for p in model.parameters())
+    num_score_model_parameters = param_count(model)
 
     typer.echo(f"Model has {num_score_model_parameters} parameters")
 
+    size_all_mb = model_size(model)
+
+    typer.echo("model size: {:.3f}MB".format(size_all_mb))
+
 
 if __name__ == "__main__":
     app()
diff --git a/bin/model-size b/bin/model-size
index d38f6a090..1acb7efdb 100755
--- a/bin/model-size
+++ b/bin/model-size
@@ -27,6 +27,8 @@ from ml_downscaling_emulator.score_sde_pytorch.models import (  # noqa: F401
     normalization,  # noqa: F401
 )  # noqa: F401
 
+from ml_downscaling_emulator.utils import model_size, param_count
+
 logger = logging.getLogger()
 logger.setLevel("INFO")
 
@@ -58,12 +60,16 @@ def main(
     config_path = os.path.join(workdir, "config.yml")
     config = load_config(config_path)
     score_model, location_params = load_model(config)
-    num_score_model_parameters = sum(p.numel() for p in score_model.parameters())
-    num_location_parameters = sum(p.numel() for p in location_params.parameters())
+    num_score_model_parameters = param_count(score_model)
+    num_location_parameters = param_count(location_params)
 
     typer.echo(f"Score model has {num_score_model_parameters} parameters")
     typer.echo(f"Location parameters have {num_location_parameters} parameters")
 
+    size_all_mb = sum(model_size(model) for model in [score_model, location_params])
+
+    typer.echo("model size: {:.3f}MB".format(size_all_mb))
+
 
 if __name__ == "__main__":
     app()
diff --git a/src/ml_downscaling_emulator/utils.py b/src/ml_downscaling_emulator/utils.py
new file mode 100644
index 000000000..a15ab7a54
--- /dev/null
+++ b/src/ml_downscaling_emulator/utils.py
@@ -0,0 +1,18 @@
+"""Helper methods"""
+
+
+def param_count(model):
+    """Count the number of parameters in a model."""
+    return sum(p.numel() for p in model.parameters())
+
+
+def model_size(model):
+    """Compute size in memory of model in MB."""
+    param_size = sum(
+        param.nelement() * param.element_size() for param in model.parameters()
+    )
+    buffer_size = sum(
+        buffer.nelement() * buffer.element_size() for buffer in model.buffers()
+    )
+
+    return (param_size + buffer_size) / 1024**2
diff --git a/tests/deterministic/smoke-test b/tests/deterministic/smoke-test
index d0fdc140e..2f549acf3 100755
--- a/tests/deterministic/smoke-test
+++ b/tests/deterministic/smoke-test
@@ -4,8 +4,8 @@ set -euo pipefail
 
 SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
 
-cpm_dataset="bham_gcmx-4x_1em_psl-sphum4th-temp4th-vort4th_eqvt_random-season-historic"
-gcm_dataset="bham_60km-4x_1em_psl-sphum4th-temp4th-vort4th_eqvt_random-season-historic"
+cpm_dataset="bham64_ccpm-4x_12em_psl-sphum4th-temp4th-vort4th_pr-historic"
+gcm_dataset="bham64_gcm-4x_12em_psl-sphum4th-temp4th-vort4th_pr-historic"
 workdir="output/test/unet/test-run"
 
 config_path="src/ml_downscaling_emulator/deterministic/configs/ukcp_local_12em_pr_unet.py"
diff --git a/tests/smoke-test b/tests/smoke-test
index 0c5ae3792..1895b9ea5 100755
--- a/tests/smoke-test
+++ b/tests/smoke-test
@@ -14,7 +14,7 @@ train_batch_size=2
 epoch=2
 
 rm -rf ${workdir}
-WANDB_EXPERIMENT_NAME="test" python bin/main.py --workdir ${workdir} --config ${config_path} --mode train --config.data.dataset_name=${dataset} --config.training.snapshot_freq=5 --config.training.eval_freq=100 --config.training.log_freq=50 --config.training.n_epochs=${epoch} --config.model.loc_spec_channels=${loc_spec_channels} --config.training.batch_size=${train_batch_size} --config.data.input_transform_key=stan --config.data.target_transform_key=sqrturrecen --config.data.time_inputs=True --config.model.num_scales=10
+WANDB_EXPERIMENT_NAME="test" python bin/main.py --workdir ${workdir} --config ${config_path} --mode train --config.data.dataset_name=${dataset} --config.training.snapshot_freq=5 --config.training.eval_freq=100 --config.training.log_freq=50 --config.training.n_epochs=${epoch} --config.model.loc_spec_channels=${loc_spec_channels} --config.training.batch_size=${train_batch_size} --config.data.input_transform_key=stan --config.data.target_transform_key=sqrturrecen --config.data.time_inputs=True --config.model.num_scales=10 --config.data.target_transforms.tmean150cm=stan
 
 num_samples=2
 eval_batch_size=32