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Who We Are?
The Problem We Solve
Deploying machine learning models in C++ often means wrestling with verbose boilerplate, complex APIs, and a steep learning curve. This friction slows down development and distracts from building great products. Aryorithm was created to eliminate this complexity.
Our Libraries
xTorch
A powerful, developer-friendly extension for LibTorch. Write cleaner, more intuitive code and leverage a rich set of utilities to accelerate your development workflow—from data pre-processing and tensor manipulation to model execution.
- Simplified Model Loading:
Load TorchScript models with a single, intuitive line of code. - Powerful Tensor Utilities:
Advanced functions for batching, data type conversion, and device placement. - Built-in Data Pre-processing:
Ready-to-use transforms for common tasks like image normalization and resizing. - Modern C++ Design:
Built with modern C++ principles for clean integration using CMake.
xInfer
The definitive toolkit for deploying NVIDIA® TensorRT™ engines. XInfer provides a streamlined C++ API for maximum local performance and a scalable cloud service for instant, hassle-free inference via a simple REST API.
- Zero-Overhead C++ API:
A lightweight wrapper for direct, high-performance TensorRT execution. - Custom CUDA Kernel Support:
An easy-to-use interface to register and run your own custom kernels. - Managed Cloud API:
Deploy models as a scalable REST endpoint without managing any infrastructure. - Optimized Memory Management:
Smart, efficient handling of GPU memory for high-throughput applications.
Why choose us
Why Choose Aryorithm?
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Simplified Workflow
Our intuitive APIs reduce boilerplate and let you focus on logic, not setup.
Learn More
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Embedding Systems
Use C++ performance inside any type of embedding systems like Nvidia Jetson
Learn MoreSee The Difference
Features You Wish C++ AI Had. Now It Does.
Developing AI in C++ often means building foundational tools from scratch. XTtorch and XInfer provide the high-level, production-ready components that are missing from the core libraries, allowing you to focus on your application, not the plumbing.
Standard C++
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- Verbose, Low-Level APIs that require significant boilerplate for fundamental tasks like model loading and device management.
- Fragmented Data Pipelines forcing developers to mix libraries and perform manual tensor math for pre-processing.
- Raw, Unstructured Outputs where models return basic tensors that require manual parsing of scores and indices.
- Manual GPU Resource Management that makes the developer responsible for all complex CUDA memoryallocation and data transfers.
- Complex Extensibility where integrating custom CUDA kernels requires fighting the difficult native TensorRT Plugin API.
- The 'Last Mile' Deployment Gap where there is no native path to expose a model as a web service without a massive DevOps effort.
With Our Library
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- Expressive, High-Level API with single-line functions for complex operations.
- Integrated Data Processing with fluent, chainable pipelines for transforming data.
- C++ Idiomatic Results via utilities that return clean, structured std::vectors of prediction objects.
- Automated GPU Memory Management which handles all CUDA boilerplate behind a simple .infer() call.
- Simplified Plugin API providing a streamlined interface to register and use your own custom CUDA kernels.
- Instant Cloud Deployment through an optional service that converts any TensorRT engine into a scalable REST API.
Ready to accelerate your C++ development?
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