Trnrt

TrnRT is a high-performance open-source inference engine designed to run large language models (LLMs) like GLM-5.3-Flash on AWS Trainium2 hardware. It leverages advanced techniques—such as NKI megakernels and optimized prompt caching—to deliver rapid model responses, even with massive, million-token contexts. The engine achieves notable throughput and cost efficiency, making it suitable for demanding workloads such as multi-agent coding environments and long-context applications.

The system supports OpenAI-compatible APIs, enabling teams to integrate it into existing toolchains with minimal friction. Extensive benchmarking data shows that a single AWS trn2.48xlarge instance can serve nearly 29 agent requests per minute, process 3.57 million tokens per minute, and handle highly cached, agent-driven sessions typical in complex software development workflows.

TrnRT is tailored for users requiring scalable, cost-efficient model inference on the cloud, with a particular emphasis on developer productivity and deployment flexibility. Its open model serving and compatibility with popular SDKs across languages like C++, Swift, and Kotlin make it accessible for integration into diverse environments, from cloud-native backend systems to edge device inference pipelines.

TrnRT is built for technical leaders and engineering teams at companies deploying large-scale AI models in production. It matters most for CTOs, AI infrastructure leads, and backend engineers who run long-context LLM applications—such as agentic development environments, coding tools, and customer-facing AI assistants—in cloud environments.

Organizations already using AWS or seeking optimized inference on Trainium2 hardware will find value in TrnRT's cost-effective performance and its compatibility with standard APIs and SDKs. DevOps teams and IT administrators responsible for the large-scale, reliable delivery of AI services can use it to minimize serving latency and operational costs in high-throughput settings.