{"uid":"cap_sqqt6KtIEU7pDYDG0pmO9","slug":"tensorrt-model-compiler-mcp-f82df85d","name":"TensorRT Model Compiler MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/tensorrt-model-compiler-mcp/api/process","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"payload":{"type":"string"}}},"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.3","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.3/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.3","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_MYXG7aNPAsjPnZamUZCDA","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.3","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Compiles and optimizes neural network models using NVIDIA TensorRT for accelerated inference deployment","exampleAgentPrompt":"Can you compile my ONNX model into a TensorRT-optimized engine so I can deploy it for fast GPU inference? Here's the model data: <base64-encoded-model>.","exampleUseCases":[{"title":"Deploy real-time object detection model","prompt":"I need to compile my YOLO object detection ONNX model into a TensorRT engine for low-latency inference on our edge GPU servers — here's the model definition data."},{"title":"Accelerate LLM inference for production","prompt":"Can you run TensorRT compilation on this transformer model so we can cut inference time before we push it to production? The model serialization data is attached."},{"title":"Convert research model for embedded GPU","prompt":"I have a trained image classification model I want to optimize with TensorRT for deployment on a Jetson device — please compile it using this model data string."}],"resultDescription":"Returns a TensorRT-compiled engine or optimized model artifact suitable for high-performance GPU inference, likely including the compiled binary plan and potentially metadata about optimization results such as layer fusion and precision settings.","failureModes":["Unsupported model architecture or layer types not compatible with TensorRT","Malformed or invalid model data in the input string","Model too large to compile within service constraints","Missing required data field in request body","Compilation timeout for very large or complex models","Incompatible precision or hardware target settings"],"whenToPreferThis":"Choose this endpoint when you need to compile or optimize a neural network model specifically for NVIDIA GPU-accelerated inference using TensorRT, particularly for production deployment requiring low latency. Prefer this over generic model serving endpoints when GPU inference speed is critical and TensorRT compatibility is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:50:06.571Z","isFirstParty":false}