{"id":"nvidia-tensorrt-llm","name":"TensorRT-LLM","homepage":"https://nvidia.github.io/TensorRT-LLM","repo_url":"https://github.com/NVIDIA/TensorRT-LLM","category":"ai-ml","subcategories":[],"tags":["ai-ml","llm-serving","inference","nvidia","tensorrt","cuda","gpu","python","moe"],"what_it_does":"TensorRT-LLM is an open-source Python/C++ toolkit for building and running optimized LLM inference on NVIDIA GPUs. It provides a Python API to define models and build high-performance inference runtimes/engines, along with serving/orchestration components and performance-focused optimizations.","use_cases":["High-throughput LLM inference on NVIDIA GPUs (batching, multi-GPU setups)","Low-latency LLM serving and experimentation with inference optimizations (e.g., KV-cache and attention variants)","Model deployment pipelines that want TensorRT-optimized engines for production GPU inference","Research/engineering exploration of LLM inference performance techniques (quantization, attention optimizations, parallelism/MoE)"],"not_for":["General-purpose CPU-only inference without NVIDIA GPU resources","Applications that require a simple hosted API with managed authentication/quotas","Teams needing a lightweight “drop-in” HTTP API client; this is primarily a local/cluster GPU inference toolkit","Use cases that cannot tolerate GPU/driver/CUDA/TensorRT build and runtime complexity"],"best_when":"You have NVIDIA GPUs and want to build TensorRT-optimized LLM engines for performant inference and/or integrate them into your own serving stack (often alongside Triton or similar).","avoid_when":"You need a turnkey SaaS API, strong managed security controls out-of-the-box, or a minimal-setup experience with no CUDA/TensorRT environment requirements.","alternatives":["NVIDIA Triton Inference Server with backend optimizations (where appropriate)","vLLM (often for GPU inference orchestration)","TensorFlow Serving / TorchServe (less focused on TensorRT-specific optimizations)","Other inference optimization toolkits (vendor or community)"],"af_score":51.5,"security_score":25.8,"reliability_score":35.0,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T13:20:53.796990+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":true,"sdk_languages":["Python","C++"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":[],"oauth":false,"scopes":false,"notes":"No service-level API authentication described in the provided content; this appears to be a local/cluster inference toolkit rather than a hosted API."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing information in the provided materials; repo appears open-source."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":51.5,"security_score":25.8,"reliability_score":35.0,"mcp_server_quality":0.0,"documentation_accuracy":75.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":95.0,"rate_limit_clarity":0.0,"tls_enforcement":0.0,"auth_strength":20.0,"scope_granularity":10.0,"dependency_hygiene":45.0,"secret_handling":60.0,"security_notes":"Based on provided content, there is no evidence of networked API security controls (TLS/auth/rate limiting). As a local/engine-building toolkit, the main security concerns are supply-chain/build dependency management and operational security in your environment (keeping secrets out of logs/build scripts). Dependency hygiene cannot be verified from the provided excerpts.","uptime_documented":0.0,"version_stability":60.0,"breaking_changes_history":50.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["This is GPU/stack-heavy (CUDA/TensorRT/PyTorch compatibility and build/runtime requirements), so “agent integration” is more about correct environment and invocation patterns than calling a stable web API.","Long-running or resource-intensive operations may fail due to GPU memory, kernel build issues, or engine compatibility; agents should expect environment-specific errors rather than consistent HTTP-style responses."]}}