diff --git a/tensorrt_llm/bench/benchmark/low_latency.py b/tensorrt_llm/bench/benchmark/low_latency.py
index fd701700a29..af86fb2b1e5 100644
--- a/tensorrt_llm/bench/benchmark/low_latency.py
+++ b/tensorrt_llm/bench/benchmark/low_latency.py
@@ -13,6 +13,7 @@
 
 from tensorrt_llm import LLM as PyTorchLLM
 from tensorrt_llm._tensorrt_engine import LLM
+from tensorrt_llm._torch.auto_deploy import LLM as AutoDeployLLM
 from tensorrt_llm.bench.benchmark.utils.asynchronous import async_benchmark
 from tensorrt_llm.bench.benchmark.utils.general import generate_warmup_dataset
 from tensorrt_llm.bench.benchmark.utils.processes import IterationWriter
@@ -298,7 +299,20 @@ def latency_command(
             kwargs["pytorch_backend_config"].enable_iter_perf_stats = True
 
         if runtime_config.backend == 'pytorch':
+            if kwargs.pop("extended_runtime_perf_knob_config", None):
+                logger.warning(
+                    "Ignore extended_runtime_perf_knob_config for pytorch backend."
+                )
             llm = PyTorchLLM(**kwargs)
+        elif runtime_config.backend == "_autodeploy":
+            if kwargs.pop("extended_runtime_perf_knob_config", None):
+                logger.warning(
+                    "Ignore extended_runtime_perf_knob_config for _autodeploy backend."
+                )
+            kwargs["world_size"] = kwargs.pop("tensor_parallel_size", None)
+            kwargs.pop("pipeline_parallel_size", None)
+
+            llm = AutoDeployLLM(**kwargs)
         else:
             llm = LLM(**kwargs)
 
diff --git a/tensorrt_llm/bench/benchmark/throughput.py b/tensorrt_llm/bench/benchmark/throughput.py
index 27f845ee5e3..b1b30125d37 100755
--- a/tensorrt_llm/bench/benchmark/throughput.py
+++ b/tensorrt_llm/bench/benchmark/throughput.py
@@ -255,25 +255,25 @@ def throughput_command(
     logger.info("Preparing to run throughput benchmark...")
     # Parameters from CLI
     # Model, experiment, and engine params
-    dataset_path: Path = params.pop("dataset")
-    eos_id: int = params.pop("eos_id")
+    dataset_path: Path = params.get("dataset")
+    eos_id: int = params.get("eos_id")
     warmup: int = params.get("warmup")
-    num_requests: int = params.pop("num_requests")
-    max_seq_len: int = params.pop("max_seq_len")
+    num_requests: int = params.get("num_requests")
+    max_seq_len: int = params.get("max_seq_len")
     model: str = bench_env.model
     checkpoint_path: Path = bench_env.checkpoint_path or bench_env.model
-    engine_dir: Path = params.pop("engine_dir")
-    concurrency: int = params.pop("concurrency")
+    engine_dir: Path = params.get("engine_dir")
+    concurrency: int = params.get("concurrency")
     backend: str = params.get("backend")
-    modality: str = params.pop("modality")
-    max_input_len: int = params.pop("max_input_len")
+    modality: str = params.get("modality")
+    max_input_len: int = params.get("max_input_len")
     model_type = get_model_config(model, checkpoint_path).model_type
 
     # Reporting options
-    report_json: Path = params.pop("report_json")
-    output_json: Path = params.pop("output_json")
-    request_json: Path = params.pop("request_json")
-    iteration_log: Path = params.pop("iteration_log")
+    report_json: Path = params.get("report_json")
+    output_json: Path = params.get("output_json")
+    request_json: Path = params.get("request_json")
+    iteration_log: Path = params.get("iteration_log")
     iteration_writer = IterationWriter(iteration_log)
 
     # Runtime kwargs and option tracking.
@@ -340,15 +340,15 @@ def throughput_command(
     engine_tokens = exec_settings["settings_config"]["max_num_tokens"]
 
     # Runtime Options
-    runtime_max_bs = params.pop("max_batch_size")
-    runtime_max_tokens = params.pop("max_num_tokens")
+    runtime_max_bs = params.get("max_batch_size")
+    runtime_max_tokens = params.get("max_num_tokens")
     runtime_max_bs = runtime_max_bs or engine_bs
     runtime_max_tokens = runtime_max_tokens or engine_tokens
-    kv_cache_percent = params.pop("kv_cache_free_gpu_mem_fraction")
-    beam_width = params.pop("beam_width")
-    streaming: bool = params.pop("streaming")
-    enable_chunked_context: bool = params.pop("enable_chunked_context")
-    scheduler_policy: str = params.pop("scheduler_policy")
+    kv_cache_percent = params.get("kv_cache_free_gpu_mem_fraction")
+    beam_width = params.get("beam_width")
+    streaming: bool = params.get("streaming")
+    enable_chunked_context: bool = params.get("enable_chunked_context")
+    scheduler_policy: str = params.get("scheduler_policy")
 
     # Update configuration with runtime options
     exec_settings["settings_config"]["kv_cache_percent"] = kv_cache_percent