Optimizing LLM Inference on SageMaker with BentoML

Enterprises are increasingly opting to self-host large language models (LLMs) to maintain data sovereignty and customize models for specific needs, despite the complexities involved. Amazon SageMaker AI simplifies this process by managing infrastructure, allowing users to focus on optimizing model performance. BentoML’s LLM-Optimizer further aids this by automating the benchmarking of different parameter configurations, helping to find optimal settings for latency and throughput. This approach is crucial for organizations aiming to balance performance and cost while maintaining control over their AI deployments.

The integration of large language models (LLMs) into applications via API calls has simplified the process of leveraging AI capabilities. However, many enterprises prefer self-hosting their models to ensure data sovereignty and enable model customization. Data sovereignty is crucial for regulatory compliance and protecting sensitive information, while customization allows models to be fine-tuned for specific industry needs. Amazon SageMaker AI simplifies the infrastructure management of self-hosting by handling the provisioning and scaling of GPU resources, allowing teams to focus on optimizing model performance. This managed approach reduces the complexity of deploying LLMs, but achieving optimal performance still requires careful configuration of parameters like tensor parallelism and batch size.

BentoML’s LLM-Optimizer addresses the challenge of finding the right configuration for LLM inference on Amazon SageMaker by automating the benchmarking process. This tool systematically explores different parameter configurations to identify the optimal setup that meets specific service level objectives, such as latency and throughput targets. By automating this process, LLM-Optimizer eliminates the need for manual trial-and-error, saving time and resources. The tool allows users to define constraints and then applies the optimal configurations directly to the SageMaker AI endpoint, ensuring a seamless transition from development to production. This approach is particularly beneficial for ML engineers and system architects who need to balance performance, cost, and user experience in real-world deployments.

Understanding the trade-offs between throughput and latency is essential for optimizing LLM inference. As throughput increases, latency tends to rise due to larger batch sizes and more concurrent requests. The challenge lies in finding the optimal configuration across multiple parameters, such as tensor parallelism degree and concurrency limits, while respecting hardware constraints like GPU memory and compute bandwidth. The roofline model helps visualize these trade-offs by plotting throughput against arithmetic intensity, revealing whether an application is bottlenecked by memory bandwidth or computational capacity. By providing a systematic approach to explore these configurations, LLM-Optimizer enables engineers to make informed decisions and achieve a balanced LLM deployment.

Practical application of these concepts involves deploying models like Qwen-3-4B on Amazon SageMaker AI, defining realistic workload constraints, and exploring various serving parameter combinations. Through theoretical analysis and empirical benchmarking, LLM-Optimizer helps identify configurations that balance latency, throughput, and cost. The tool generates artifacts such as Pareto dashboards and JSON files that summarize benchmark results, providing valuable insights into the performance trade-offs of different configurations. By leveraging these insights, teams can optimize their LLM deployments, ensuring they meet the desired performance criteria while maintaining cost-effectiveness and a positive user experience. This process underscores the importance of systematic optimization in deploying AI models in production environments.

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