diff --git a/benchmark/camelyon/benchmarks.py b/benchmark/camelyon/benchmarks.py
index 76ea8176..228d29c0 100644
--- a/benchmark/camelyon/benchmarks.py
+++ b/benchmark/camelyon/benchmarks.py
@@ -64,7 +64,7 @@ def fed_avg(params: dict, train_folder: Path, test_folder: Path):
         mode=exp_params["mode"],
         cp_name=exp_params["cp_name"],
         cancel_cp=exp_params["cancel_cp"],
-        torch_gpu=exp_params["torch_gpu"],
+        use_gpu=exp_params["use_gpu"],
     )
 
     if exp_params["skip_pure_torch"]:
diff --git a/benchmark/camelyon/common/utils.py b/benchmark/camelyon/common/utils.py
index 1ed69494..b971fe11 100644
--- a/benchmark/camelyon/common/utils.py
+++ b/benchmark/camelyon/common/utils.py
@@ -81,7 +81,7 @@ def parse_params() -> dict:
         default=False,
         help="Remote only: cancel the CP after registration",
     )
-    parser.add_argument("--torch-gpu", action="store_true", help="Use PyTorch with GPU/CUDA support")
+    parser.add_argument("--use-gpu", action="store_true", help="Use PyTorch with GPU/CUDA support")
     parser.add_argument(
         "--skip-pure-torch",
         action="store_true",
@@ -107,7 +107,7 @@ def parse_params() -> dict:
     params["nb_test_data_samples"] = args.nb_test_data_samples
     params["data_path"] = args.data_path
     params["cancel_cp"] = args.cancel_cp
-    params["torch_gpu"] = args.torch_gpu
+    params["use_gpu"] = args.use_gpu
     params["skip_pure_torch"] = args.skip_pure_torch
     params["cp_name"] = args.cp_name
 
diff --git a/benchmark/camelyon/workflows.py b/benchmark/camelyon/workflows.py
index 23f805be..43a6a2ef 100644
--- a/benchmark/camelyon/workflows.py
+++ b/benchmark/camelyon/workflows.py
@@ -44,7 +44,7 @@ def substrafl_fed_avg(
     asset_keys_path: Path,
     cp_name: Optional[str],
     cancel_cp: bool = False,
-    torch_gpu: bool = False,
+    use_gpu: bool = False,
 ) -> benchmark_metrics.BenchmarkResults:
     """Execute Weldon algorithm for a fed avg strategy with substrafl API.
 
@@ -68,7 +68,7 @@ def substrafl_fed_avg(
             Otherwise, all present keys in this fill will be reused per Substra in remote mode.
         cp_name ben): (Optional[str]): Compute Plan name to display
         cancel_cp (bool): if set to True, the CP will be canceled as soon as it's registered. Only work for remote mode.
-        torch_gpu (bool): Use GPU default index for pytorch
+        use_gpu (bool): Use GPU for Dependency object
     Returns:
         dict: Results of the experiment.
     """
@@ -97,7 +97,7 @@ def substrafl_fed_avg(
         "torch==2.3.0",
         "scikit-learn==1.5.1",
     ]
-    if not torch_gpu:
+    if not use_gpu:
         pypi_dependencies += ["--extra-index-url https://download.pytorch.org/whl/cpu"]
 
     # Dependencies
@@ -108,6 +108,7 @@ def substrafl_fed_avg(
         # Keeping editable_mode=True to ensure nightly test benchmarks are ran against main substrafl git ref
         editable_mode=True,
         compile=True,
+        use_gpu=use_gpu,
     )
 
     # Metrics
diff --git a/changes/244.fixed b/changes/244.fixed
new file mode 100644
index 00000000..5332e812
--- /dev/null
+++ b/changes/244.fixed
@@ -0,0 +1 @@
+Actually trigger the GPU docker configuration with `use_gpu` flag when running Camelyon benchmark