Ultralytics YOLO INT8 build failure of TensorRT 10.16 (MyelinCheckException) on RTX PRO 6000 Blackwell, regression from 10.15 #4906

Description

Building an implicit INT8 engine (BuilderFlag.INT8 + BuilderFlag.FP16, with per-tensor dynamic ranges or an IInt8Calibrator) from Ultralytics YOLO YOLO26n and YOLO11n ONNX models fails on TensorRT 10.16.1.11 with an internal Myelin error. The same script succeeds on TensorRT 10.15.1.29, so this looks like a regression in 10.16:

Internal Error: MyelinCheckException: utils.cpp:4054: CHECK(0) failed.
[TRT] [E] Error Code: 9: Skipping tactic 0x0000000000000000 due to exception [myelin_graph.h:1250: attachExceptionMsgToGraph] MyelinCheckException: utils.cpp:4054: CHECK(0) failed.  In compileGraph at /_src/optimizer/myelin/codeGenerator.cpp:1783
[TRT] [E] IBuilder::buildSerializedNetwork: Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[Quantize 0...output0_castOut]}. In computeCosts at /_src/optimizer/common/tactic/optimizer.cpp:4265)

FP16 builds of the same models succeed on 10.16.1.11. The Ultralytics export path (yolo export model=yolo26n.pt format=engine quantize=8 data=coco128.yaml) uses an IInt8Calibrator with MINMAX_CALIBRATION on TensorRT 10.x (source). On 10.16.1.11 it fails with the same error for YOLO26n and YOLO26s.

TensorRT 11 removed the implicit INT8 calibrator and DLA, so Ultralytics users who need either still build with TensorRT 10.x. See the Ultralytics TensorRT integration guide.

Environment

TensorRT Version: 10.16.1.11 (fails), 10.15.1.29 (works) (tensorrt-cu13 pip wheels)

NVIDIA GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition (not tested on other GPUs)

NVIDIA Driver Version: 595.99.02

CUDA Version: 13.2

CUDNN Version: N/A

Operating System: Ubuntu 24.04.5 LTS

Python Version (if applicable): 3.12.3

PyTorch Version (if applicable): 2.15.0 (CPU, used only for the ONNX export)

Other: ultralytics 8.4.196, onnx 1.21.0, ONNX opset 18

Baremetal or Container (if so, version): Baremetal

Relevant Files

Model link: yolo26n.pt and yolo11n.pt. The ONNX files are generated by the export command below.

Steps To Reproduce

Commands or scripts:

pip install ultralytics==8.4.196 tensorrt-cu13==10.16.1.11
yolo export model=yolo26n.pt format=onnx imgsz=640  # -> yolo26n.onnx
python build_int8_implicit.py

build_int8_implicit.py:

# Implicit INT8 (calibration-style) build of the FP32 ONNX with INT8 + FP16 builder flags.
# Static dynamic ranges stand in for a calibrator; the IInt8Calibrator path in Ultralytics fails identically.
import tensorrt as trt

logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)
network = builder.create_network(0)
config = builder.create_builder_config()
assert trt.OnnxParser(network, logger).parse_from_file("yolo26n.onnx")
config.set_flag(trt.BuilderFlag.INT8)
config.set_flag(trt.BuilderFlag.FP16)
for i in range(network.num_inputs):
    network.get_input(i).dynamic_range = (0.0, 1.0)
for i in range(network.num_layers):
    layer = network.get_layer(i)
    for j in range(layer.num_outputs):
        layer.get_output(j).dynamic_range = (-8.0, 8.0)
plan = builder.build_serialized_network(network, config)
print(f"TensorRT {trt.__version__}: INT8 build {'succeeded' if plan else 'FAILED'}")

Output:

TensorRT 10.16.1.11: INT8 build FAILED
TensorRT 10.15.1.29: INT8 build succeeded

Have you tried the latest release?: 10.16.1.11 is the latest 10.x release. TensorRT 11.x no longer supports implicit INT8 builds.

Can this model run on other frameworks?: Yes. The ONNX models run in ONNX Runtime, and they build and run on TensorRT 10.15.1.29 (INT8) and 10.16.1.11 (FP16).