diff --git a/tensorrt_convert.py b/tensorrt_convert.py
index e7e3e92..2502c01 100644
--- a/tensorrt_convert.py
+++ b/tensorrt_convert.py
@@ -5,6 +5,7 @@
 import comfy.model_management
 
 import tensorrt as trt
+from .tensorrt_error_recorder import TrTErrorRecorder
 import folder_paths
 from tqdm import tqdm
 
@@ -284,6 +285,7 @@ def forward(self, x, timesteps, context, y=None):
         # TRT conversion starts here
         logger = trt.Logger(trt.Logger.INFO)
         builder = trt.Builder(logger)
+        builder.error_recorder = TrTErrorRecorder()
 
         network = builder.create_network(
             1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
diff --git a/tensorrt_error_recorder.py b/tensorrt_error_recorder.py
new file mode 100644
index 0000000..86c2c81
--- /dev/null
+++ b/tensorrt_error_recorder.py
@@ -0,0 +1,47 @@
+import logging
+import threading
+
+import tensorrt as trt
+
+# TensorRT Python API Docs: https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Core/ErrorRecorder.html
+#
+# NOTE: TensorRT does not attempt to marshall errors across threads, so IErrorRecorder implementations must be thread-safe to allow for the possibility of errors occuring on multiple threads.
+class TrTErrorRecorder(trt.IErrorRecorder):
+    def __init__(self):
+        self.lock = threading.Lock()
+        self.error_list = []
+        super().__init__()
+
+    def clear(self):
+        with self.lock:
+            self.error_list = []
+
+    def get_error_code(self, error_index):
+        with self.lock:
+            if error_index >= len(self.error_list) or error_index < 0:
+                raise IndexError(f'Invalid error index "{error_index}"')
+
+            return self.error_list[error_index]['error_code']
+
+    def get_error_desc(self, error_index):
+        with self.lock:
+            if error_index >= len(self.error_list) or error_index < 0:
+                raise IndexError(f'Invalid error index "{error_index}"')
+
+            return self.error_list[error_index]['error_desc']
+
+    def has_overflowed(self):
+        return False
+
+    def num_errors(self):
+        with self.lock:
+            return len(self.error_list)
+
+    def report_error(self, error_code, error_desc):
+        logging.error(f"TensorRT has encountered an error. ErrorCode: {error_code}. ErrorDesc: {error_desc}")
+
+        with self.lock:
+            self.error_list.append({'error_code': error_code, 'error_desc': error_desc})
+
+        # TODO Future: return True for errors we consider 'fatal', which hints to TensorRT to stop execution.
+        return False
diff --git a/tensorrt_loader.py b/tensorrt_loader.py
index 8ceff29..d03b489 100644
--- a/tensorrt_loader.py
+++ b/tensorrt_loader.py
@@ -18,11 +18,13 @@
         [os.path.join(folder_paths.models_dir, "tensorrt")], {".engine"})
 
 import tensorrt as trt
+from .tensorrt_error_recorder import TrTErrorRecorder
 
 trt.init_libnvinfer_plugins(None, "")
 
 logger = trt.Logger(trt.Logger.INFO)
 runtime = trt.Runtime(logger)
+runtime.error_recorder = TrTErrorRecorder()
 
 # Is there a function that already exists for this?
 def trt_datatype_to_torch(datatype):
@@ -35,11 +37,28 @@ def trt_datatype_to_torch(datatype):
     elif datatype == trt.bfloat16:
         return torch.bfloat16
 
+def check_for_trt_errors(runtime):
+    num_deserialize_errors = runtime.error_recorder.num_errors()
+    if num_deserialize_errors == 0:
+        return
+
+    error_string = ''
+    for error_index in range(num_deserialize_errors):
+        if error_index > 0:
+            error_string += "\n"
+        error_string += runtime.error_recorder.get_error_desc(error_index)
+    runtime.error_recorder.clear()
+    raise RuntimeError(f'Failed to load TensorRT engine: {error_string}')
+
 class TrTUnet:
     def __init__(self, engine_path):
         with open(engine_path, "rb") as f:
             self.engine = runtime.deserialize_cuda_engine(f.read())
+        check_for_trt_errors(runtime)
+
         self.context = self.engine.create_execution_context()
+        check_for_trt_errors(runtime)
+
         self.dtype = torch.float16
 
     def set_bindings_shape(self, inputs, split_batch):