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Accelerating global growth through the industry's most integrated cloud-based Translation Management System.

The high-performance C++ Neural Machine Translation framework for efficient training and inference.
Marian is an efficient Neural Machine Translation framework, written in pure C++ with minimal dependencies. Developed primarily by the Microsoft Translator team and researchers at the University of Edinburgh and Adam Mickiewicz University, it is engineered for high training speeds and optimized inference. Unlike frameworks that rely on Python-heavy abstractions, Marian utilizes its own built-in auto-differentiation engine and is optimized for multi-GPU training environments using asynchronous SGD. Its architecture is specifically designed to support the Transformer model at scale, providing industry-leading throughput for both production and academic research. As of 2026, Marian remains the backbone of several major commercial translation services due to its low memory footprint and high-speed execution on both CUDA-enabled GPUs and standard x86 CPUs. It excels in knowledge distillation, allowing enterprises to compress massive teacher models into highly efficient student models for edge deployment. The tool's robustness is evidenced by its integration into the Bergamot project, enabling private, client-side translation within browsers like Firefox.
Custom engine that allows for reverse-mode automatic differentiation without the overhead of external libraries like PyTorch.
Accelerating global growth through the industry's most integrated cloud-based Translation Management System.
Enterprise-grade Neural Machine Translation with local data residency and 100+ language support.
The industry-standard multi-engine translation aggregator for professional linguistic benchmarking.
Enterprise-grade neural machine translation via unified API and batch file processing.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Implementation of Hogwild!-style asynchronous stochastic gradient descent for multi-GPU scaling.
Native support for teacher-student training to compress large models into smaller, faster versions.
Ability to force the decoder to include specific terms or phrases in the translation output.
Supports 8-bit integer quantization and gemmlowp for CPU-based inference.
Highly optimized implementation of the Transformer-base and Transformer-big architectures.
Highly customizable beam search algorithm with support for ensembling multiple models.
General-purpose MT (like Google Translate) fails to capture industry-specific terminology for a legal firm.
Registry Updated:2/7/2026
Deploy via marian-decoder API.
Users need to translate web pages without sending data to a third-party server.
Researchers need to test a new attention mechanism within a high-speed framework.