bmdpat is a collection of local-first AI tools and resources aimed at builders who run open AI models on controlled compute environments. The platform centers on providing practical instrumentation, benchmarking, and operational guidance for deploying and measuring open models, particularly on consumer GPUs such as the RTX 5090, as well as on rented accelerators, private cloud, and GPU-backed clusters. It addresses the needs of those seeking to manage and optimize local AI workflows, offering tools that help monitor performance, resource usage, and operational constraints.
Among its offerings are benchmark reports that detail model performance metrics such as token generation speed, prompt size, and peak VRAM usage for various models and workloads. The platform publishes both successful and failed runs, maintaining a record of failure logs and providing transparency into operational challenges. Users can access build notes and field notes that share practical advice, such as the importance of pinning context size to avoid costly model reloads. The archive serves as a reference for local-first experimentation and model deployment on controlled compute.
bmdpat features a suite of twelve live tools, including a VRAM Calculator, Model Picker, Quantization Compare, Local LLM Toolkit Pro, AgentGuard, and Agent Roadmap Scanner. These tools are accessible via web browsers, with some available in both free and pro versions, and others such as AgentGuard provided as a free Python package. AgentGuard offers runtime guardrails to enforce budget, loop, timeout, and rate limits before a run begins, supporting safer and more predictable AI operations. The Local LLM Toolkit Pro is available with a 7-day trial and a subscription fee, while browser-based tools offer free and pro tiers.
The service is developed and maintained by Patrick Hughes, operating as a solo indie maker under BMD Pat LLC. Regular updates, lab notes, and benchmark results are published in the 5090 Reports, providing ongoing insights for builders working with open models on local or controlled compute infrastructure.
In the AI & ML space, Bmdpat takes a focused approach. It focuses on benchmarking and monitoring open AI models on local or controlled compute environments. Bmdpat is an open-source project aimed at AI engineers and researchers. It runs on the command line.
Behind Bmdpat is Patrick Hughes, based in the United States, and the product first shipped in 2026. Among its 5 catalogued features are benchmarking, VRAM fit checks, and failure logs.
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