Custom LLM Hosting & Red-Teaming Infrastructure

The Challenge

A client required a secure, scalable, and high-performance infrastructure for hosting Large Language Models (LLMs) specifically for red-teaming purposes. The solution needed to meet industry-standard latency requirements and provide robust tools for evaluating LLM vulnerabilities.

Our Solution

Ferociter engineered a custom LLM hosting and red-teaming apparatus on AWS:

1. Scalable LLM Hosting: We designed a containerized architecture using AWS services (e.g., EKS, SageMaker) for efficient deployment and scaling of various LLMs.
2. Low-Latency Inference: Optimized inference endpoints were developed to meet stringent latency standards, crucial for effective red-teaming interactions.
3. Monitoring & Analytics: Integrated monitoring and analytics dashboards to track model performance, resource utilization, and red-teaming effectiveness.

The core infrastructure was built using Python for backend services and model interaction, with TypeScript for developing the red-teaming interface and tooling.

Results & Impact

The custom infrastructure provided the client with a powerful and flexible platform to conduct thorough red-teaming of LLMs, identify vulnerabilities, and enhance model safety and robustness before deployment.