BentoML's llm-optimizer Automates LLM Inference Benchmarking and Tuning
BentoML has published llm-optimizer, an open-source framework aimed at simplifying benchmarking and performance tuning for self-hosted large language models. The tool is designed to reduce the manual trial-and-error that typically accompanies efforts to optimize latency, throughput, and cost.
Why LLM inference tuning is hard
Tuning LLM inference requires balancing many interdependent factors: batch size, inference framework choice such as vLLM or SGLang, tensor parallelism, sequence lengths, and hardware utilization. Each parameter can affect performance in different ways, so identifying the right combination for a target latency, throughput, and GPU efficiency is difficult. Many teams still rely on repetitive experiments that are slow, inconsistent, and costly when run at scale on self-hosted infrastructure.
What llm-optimizer does
llm-optimizer gives engineers a structured way to explore the performance space. Rather than guessing configurations, teams can run systematic benchmarks and automated searches across configuration options. The framework focuses on reproducible, constraint-driven optimization so teams can target specific operational goals such as a maximum time-to-first-token or a minimum throughput.
Core capabilities
- Standardized benchmarking across inference frameworks like vLLM and SGLang
- Constraint-driven tuning, for example surfacing only configurations where time-to-first-token is below a given threshold
- Automated parameter sweeps to find optimal settings for specific objectives
- Dashboards and visualizations that make tradeoffs clear for latency, throughput, and GPU utilization
Open-source availability
The framework is open-source and hosted on GitHub. BentoML also provides tutorials, example notebooks, and code to help teams reproduce benchmarks and adapt the tooling to their own models and hardware.
LLM Performance Explorer: explore results without local benchmarking
To complement the optimizer, BentoML released the LLM Performance Explorer, a browser-based interface that uses pre-computed benchmark data produced by llm-optimizer. The Explorer lets users compare frameworks and configurations side by side, filter results by latency, throughput, or resource constraints, and interactively inspect tradeoffs without provisioning hardware.
Why this matters for deployments
As LLM adoption grows, deployment efficiency becomes a major cost and performance factor. llm-optimizer lowers the expertise and infrastructure barriers by automating searches and providing standardized benchmarks. Smaller teams gain access to optimization techniques that once required extensive infrastructure and deep specialization.
By producing reproducible results and a transparent comparison surface across models and frameworks, the tool helps close a long-standing gap in the community and makes deployment decisions more data-driven.
How to get started
Developers and operators can find the code, tutorials, and notebooks on BentoML’s GitHub repository. The LLM Performance Explorer provides a quick way to inspect benchmarked configurations for popular open-source models without running tests locally. Follow BentoML on social channels or check the project repository for the latest updates and examples.