Bench360: The 360-Degree Benchmark for Optimizing Local LLM Performance and Efficiency
By Linus Stuhlmann, Mauricio Fadel Argerich, Jonathan F\"urst
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary\
Bench360 is a new comprehensive benchmarking framework designed to simplify the complex process of optimizing local Large Language Model (LLM) inference. Addressing the limitations of existing narrow and non-user-focused benchmarks, Bench360 allows users to define custom tasks, datasets, and metrics. It automatically evaluates selected LLMs, inference engines, and quantization levels across various usage scenarios (single stream, batch, server). The tool tracks a wide array of metrics, including computing performance (latency, throughput), resource usage (energy per query), deployment aspects (cold start time), and task-specific metrics (e.g., ROUGE, F1 score). Demonstrations across diverse tasks and hardware reveal significant trade-offs, underscoring that no single configuration is universally optimal, thereby validating Bench360's utility.
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Why It Matters\
The emergence of Bench360 is a significant development for AI professionals navigating the rapidly evolving landscape of local LLM deployment. As LLMs become more democratized and privacy concerns drive a shift towards on-device or on-premise inference, the ability to efficiently configure and optimize these models is paramount. Bench360 directly addresses the \"paradox of choice\" faced by engineers and researchers, where an overwhelming number of models, engines, and quantization techniques exist without a clear path to optimal performance and resource efficiency. The finding that \"there is no single best setup\" is a critical insight, validating the need for such a comprehensive framework. This tool empowers professionals to move beyond trial-and-error, enabling data-driven decisions that balance compute performance, energy consumption, and task-specific accuracy. For MLOps engineers, it provides a crucial mechanism for robust model deployment and continuous optimization. For researchers, it offers a standardized methodology to compare different approaches transparently. Ultimately, Bench360 will accelerate the practical application of LLMs in diverse, resource-constrained, or privacy-sensitive environments, making advanced AI more accessible and sustainable.