AWS continues to push the boundaries of inference optimization, as demonstrated by several groundbreaking announcements during AWS re:Invent 2024. This post explores the latest developments in latency optimization for Bedrock and what they mean for organizations building production AI applications.

The Challenge of Production Inference

When building generative AI applications, model performance is just the beginning. For production deployments, latency becomes critical – users expect quick responses, and slow, laggy experiences can make otherwise great applications unusable. As AWS CEO Matt Garman emphasized during his keynote, achieving the right mix of model expertise, latency, and cost is crucial for successful production deployments.

Bedrock’s Latest Latency Optimizations

To address these challenges, AWS announced several key capabilities in Bedrock for optimizing inference latency:

Model Distillation

A standout announcement was the enhancement of model distillation in Bedrock. This feature allows customers to create smaller, faster versions of large language models that maintain expertise in specific domains while delivering:

• 500% faster inference speeds

• 75% lower costs compared to the original model

• Automated distillation process – customers simply provide sample prompts

The distillation process works by taking a large frontier model and using it to train a smaller, specialized model that excels at specific types of queries while being much more efficient.

Nova Models

AWS also introduced their new Nova family of models, built specifically for latency optimization. The Nova models were highlighted as “the fastest models that you’ll see with respect to latency” among the options available in Bedrock. These models will be available in a dedicated latency-optimized inference SKU.

Benefits for Production Applications

These latency optimizations can be transformative for production applications:

• Better user experiences with faster response times

• Improved economics that can make previously unfeasible use cases viable

• Maintained accuracy and capabilities for specific domains

• Simplified deployment with integrated Bedrock features

Getting Started

The new latency optimization features are available through Amazon Bedrock, allowing developers to easily experiment with and deploy faster models. The automated nature of capabilities like model distillation means teams can focus on their applications rather than the complexities of optimizing model performance.

Our Viewpoint

While most of us won’t be training models on Trainium 2, its introduction will fundamentally change how we consume AI services in production environments. The combination of more powerful models, automated reasoning checks, and improved cost efficiency means we can:

• Build more reliable AI-powered services with confidence that responses are accurate

• Take advantage of more sophisticated AI capabilities at lower costs

• Deploy AI in production workloads without compromising on performance or reliability

These are particularly important as we see increasing demand for AI integration in mission-critical systems where accuracy and cost efficiency are non-negotiable.

For those using Retrieval-Augmented Generation (RAG), the new capabilities will be transformative. RAGs require careful balancing between model performance and cost—frequently leading to compromises in accuracy and precision in order to maintain reasonable response times and budgets. Models trained on Trainium 2 will see RAG implementations that can handle larger data sets, understand more nuanced relationships between documents, and generate more contextually accurate responses – all while saving money.

This means your existing RAG investments won’t just get incrementally better; they’ll be able to handle more sophisticated use cases like processing entire technical manuals, understanding complex regulatory documents, or analyzing years of customer interaction data in a single context window. Combined with Amazon Bedrock’s new automated reasoning checks, these RAG implementations will be both more powerful and more reliable.

Organizations looking to implement generative AI in production should carefully evaluate these new capabilities as they plan their 2025 AI strategy and beyond. With proper infrastructure and operational support, here are a handful of general purpose ideas that just got a lot more accessible and cost effective with this week’s announcements: