Your Oncology AI Stack: Fully On-Prem with AMD MI300X

Architectural Precision for Sensitive Domains

You can achieve a sophisticated architecture designed with privacy and accuracy at its core using the OncoAgent framework for oncology clinical decision support. This framework introduces a dual-tier fine-tuned LLM system, comprising a 9B parameter speed-optimised model (Tier 1) for rapid processing and a 27B deep-reasoning model (Tier 2) for more complex analytical tasks. The multi-model approach is integrated into a state-of-the-art multi-agent LangGraph topology, providing an orchestrated workflow for clinical queries.

To safeguard sensitive patient information, OncoAgent incorporates a three-layer reflexion safety validator enforcing a strict Zero-PHI policy, ensuring that Protected Health Information never enters or leaves the system unencrypted or unscrubbed. This layered approach to data handling and model reasoning positions OncoAgent as a robust solution for highly regulated environments. The key features of OncoAgent include:

• A dual-tier LLM system with 9B and 27B parameter models
• A multi-agent LangGraph topology for clinical queries
• A three-layer reflexion safety validator for Zero-PHI policy enforcement

LangGraph and Multi-Agent Systems

LangGraph provides a robust framework for building stateful, multi-actor applications with large language models by representing execution as a graph. You can define a sequence of steps, where each step can be an LLM call, a tool invocation, or even another LLM acting as an agent. The system maintains state across these steps, allowing agents to remember prior interactions and results. Multi-agent systems, powered by frameworks like LangGraph, orchestrate several specialized LLMs or tools to collaborate on a complex task.

Performance and Infrastructure Implications

Your ability to achieve full-stack AI deployment on a single hardware instance stems directly from the underlying infrastructure choices. The OncoAgent models were fine-tuned using the Unsloth framework and QLoRA on a corpus of 266,854 real and synthetically generated oncological cases. This process leveraged AMD Instinct MI300X hardware, which features 192 GB of HBM3 memory. The use of sequence packing on the MI300X enabled full-dataset fine-tuning to complete in approximately 50 minutes.

The deployment strategy, encompassing OncoAgent, Featherless.ai, PyMuPDF, Gradio, ChatGPT, and Lucide, demonstrates a holistic approach to building an independent AI stack. The performance numbers underscore the AMD MI300X's capability to handle demanding AI workloads, from intensive training to real-time inference, within a self-contained environment.

What This Means For You

For your organization, this OncoAgent deployment model on AMD MI300X hardware translates directly into enhanced control and reduced external dependencies. You gain the ability to operate your entire AI workflow for oncology decision support within your own data centers, significantly mitigating concerns over data privacy, regulatory compliance, and cloud vendor lock-in. The benefits of OncoAgent include:

• Enhanced control over AI workflow
• Reduced external dependencies
• Improved data privacy and regulatory compliance

The Bottom Line for Developers

The OncoAgent framework and AMD MI300X hardware offer a robust solution for oncology clinical decision support. You can achieve rapid processing and complex analytical tasks while maintaining strict Zero-PHI policies. By leveraging this technology, you can improve the accuracy and reliability of your AI models, ultimately leading to better patient outcomes.