BentoML Released llm-optimizer: An Open-Source AI Tool for Benchmarking and Optimizing LLM Inference

BentoML has recently released llm-optimizer, an open-source framework designed to streamline the benchmarking and performance tuning of self-hosted large language models (LLMs). This tool addresses a common challenge in LLM deployment: finding optimal configurations for latency, throughput, and cost without relying on manual trial-and-error.

Challenges in Tuning LLM Performance

Tuning LLM inference is a balancing act across many moving parts, including batch size, framework choice (such as vLLM and SGLang), tensor parallelism, sequence lengths, and hardware utilization. Each of these factors can shift performance in different ways, making it difficult to find the right combination for speed, efficiency, and cost. Most teams still rely on repetitive trial-and-error testing, a process that is slow, inconsistent, and often inconclusive. For self-hosted deployments, the cost of getting it wrong is high: poorly tuned configurations can quickly lead to higher latency and wasted GPU resources.

How llm-optimizer Differs

llm-optimizer provides a structured way to explore the LLM performance landscape. It eliminates repetitive guesswork by enabling systematic benchmarking and automated searches across possible configurations.

Core capabilities include:

• Running standardized tests across inference frameworks such as vLLM and SGLang.
• Applying constraint-driven tuning, e.g., surfacing only configurations where time-to-first-token is below 200 ms.
• Automating parameter sweeps to identify optimal settings.
• Visualizing tradeoffs with dashboards for latency, throughput, and GPU utilization.

The framework is open-source and available on GitHub.

Exploring Results Without Local Benchmarks

Alongside the optimizer, BentoML released the LLM Performance Explorer, a browser-based interface powered by llm-optimizer. This tool provides pre-computed benchmark data for popular open-source models and allows users to:

• Compare frameworks and configurations side by side.
• Filter by latency, throughput, or resource thresholds.
• Browse tradeoffs interactively without provisioning hardware.

Impact on LLM Deployment Practices

As the use of LLMs grows, optimizing deployment comes down to how well inference parameters are tuned. llm-optimizer simplifies this process, granting smaller teams access to optimization techniques that previously required large-scale infrastructure and deep expertise.

By providing standardized benchmarks and reproducible results, the framework adds much-needed transparency to the LLM space. It makes comparisons across models and frameworks more consistent, addressing a long-standing gap in the community.

Ultimately, BentoML’s llm-optimizer introduces a constraint-driven, benchmark-focused method to self-hosted LLM optimization, replacing ad-hoc trial and error with a systematic and repeatable workflow.

Check out the GitHub Page for tutorials, codes, and notebooks. Also, feel free to follow us on Twitter and join our 100k+ ML SubReddit, and subscribe to our newsletter.