Troubleshooting — TensorRT-LLM (cross-section)

Full summary: Troubleshooting — TensorRT-LLM

The official NVIDIA TensorRT-LLM troubleshooting guide covering debug instrumentation, execution error diagnosis, and environment configuration for production deployments.

NVIDIA Platform Implementation Angle

This source is directly relevant to the nvidia-platform-implementation topic area as a practical operations reference for TensorRT-LLM deployments. Key contributions to this section:

• Debug Instrumentation API: register_network_output() + --enable_debug_output + --debug_mode provides a complete layer-level visibility pipeline for validating model behavior post-conversion; critical when verifying that optimized TensorRT engines reproduce expected outputs from the original HuggingFace checkpoints
• Shape Mismatch Resolution: TLLM_LOG_LEVEL=TRACE exposes the full optimization profile (min/opt/max shapes per tensor) — essential for configuring correct max_batch_size, max_input_len, and max_output_len at build time
• Multi-GPU / Slurm Deployment: mpirun -n 1 pattern for Slurm environments; --shm-size=1g --ulimit memlock=-1 Docker flags for NCCL — operational requirements for NVIDIA DGX and cluster deployments
• Error Diagnostics Reference: Tabulated mapping from error patterns (format mismatch, shape assertion, MPI init failure) to root causes and solutions

Connections

• Performance Analysis — TensorRT-LLM — performance analysis and troubleshooting share the same debug tensor instrumentation (register_network_output, debug_buffer); troubleshooting locates correctness failures, performance analysis locates throughput bottlenecks
• Performance Tuning Guide — tuning decisions (batch size, plugin selection) directly determine whether build-time memory errors described in this guide occur