TensorRT-LLM
25 MCP servers and agent skills in the TensorRT-LLM category, ranked by quality score — 25 results.
NVIDIA/TensorRT-LLM/ad-accuracy-debug
Debug AutoDeploy accuracy regressions vs a reference score (PyTorch backend or published baseline).
NVIDIA/TensorRT-LLM/ad-add-fusion-transformation
Claude Code skill (trtllm-agent-toolkit): implement or extend TensorRT-LLM AutoDeploy fusion transforms under transform/library/ in a TensorRT-LLM checkout.
NVIDIA/TensorRT-LLM/ad-conf-check
Check whether AutoDeploy YAML configs were actually applied by analyzing server logs and optionally graph dumps (AD_DUMP_GRAPHS_DIR).
NVIDIA/TensorRT-LLM/ad-graph-dump
Enable and interpret TensorRT-LLM AutoDeploy FX graph text dumps via AD_DUMP_GRAPHS_DIR.
NVIDIA/TensorRT-LLM/ad-layer-visualizer
Visualize a specific transformer decoder layer from an AutoDeploy FX graph text dump as a hierarchical DOT/PNG diagram.
NVIDIA/TensorRT-LLM/ad-model-onboard
Translates a HuggingFace model into a prefill-only AutoDeploy custom model using reference custom ops, validates with hierarchical equivalence tests.
NVIDIA/TensorRT-LLM/exec-local-compile
Compile TensorRT-LLM on a compute node inside a Docker container.
NVIDIA/TensorRT-LLM/kernel-cute-writing
Write and implement GPU kernels using NVIDIA CuTe DSL (CUTLASS 4.x Python API) — NOT for Triton, CUDA C++, or conceptual explanations.
NVIDIA/TensorRT-LLM/kernel-tileir-optimization
Optimize existing Triton kernels for NVIDIA TileIR backend on Blackwell GPUs (sm_100+).
NVIDIA/TensorRT-LLM/kernel-triton-writing
ONLY for OpenAI Triton (@triton.jit) kernel development.
NVIDIA/TensorRT-LLM/perf-host-analysis
Analyze host/CPU overhead in TensorRT-LLM inference from nsys traces.
NVIDIA/TensorRT-LLM/perf-host-optimization
Profiles and optimizes TensorRT-LLM host/CPU overhead using line_profiler (with nsys support planned).
NVIDIA/TensorRT-LLM/perf-nsight-compute-analysis
Analyze ncu (NVIDIA Nsight Compute) profiling output: SOL% bottleneck classification, roofline analysis, occupancy diagnosis, memory hierarchy analysis, warp stall analysis, metr...
NVIDIA/TensorRT-LLM/perf-nsight-systems
Nsight Systems (nsys) CLI for system-level timeline profiling.
NVIDIA/TensorRT-LLM/perf-optimization
Performance optimization coordination playbook.
NVIDIA/TensorRT-LLM/perf-torch-cuda-graphs
Apply CUDA Graphs to PyTorch workloads — API selection (torch.compile, PyTorch make_graphed_callables, TE make_graphed_callables, MCore CudaGraphManager, FullCudaGraphWrapper, m...
NVIDIA/TensorRT-LLM/perf-torch-sync-free
Identify and eliminate host-device synchronizations in PyTorch code.
NVIDIA/TensorRT-LLM/perf-workload-profiling
Code instrumentation for timing workloads.
NVIDIA/TensorRT-LLM/trtllm-code-contribution
Best practices for contributing code to TensorRT-LLM.
NVIDIA/TensorRT-LLM/trtllm-codebase-exploration
Systematic approach to exploring the TensorRT-LLM codebase before implementing new features or optimizations.
NVIDIA/TensorRT-LLM/trtllm-flashinfer-upgrade
Upgrade flashinfer-python version in TensorRT-LLM.
NVIDIA/TensorRT-LLM/trtllm-moe-develop
Review, design, and refactor TensorRT-LLM PyTorch MoE code for architecture fit, clean code, maintainability, and testability.
NVIDIA/TensorRT-LLM/trtllm-serve-config-guide
Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs.