In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking
We present a novel benchmarking methodology for evaluating offline Large Language Models (LLMs) in resource-constrained mobile robotics applications. Using an Nvidia Jetson Nano platform with 4GB RAM limitation, we demonstrate the feasibility of deploying tuned ChatGPT4All for robotic control tasks....
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138943 |
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| author | Yegin Genc Gonca Altuger-Genc Akin Tatoglu |
| author_facet | Yegin Genc Gonca Altuger-Genc Akin Tatoglu |
| author_sort | Yegin Genc |
| collection | DOAJ |
| description | We present a novel benchmarking methodology for evaluating offline Large Language Models (LLMs) in resource-constrained mobile robotics applications. Using an Nvidia Jetson Nano platform with 4GB RAM limitation, we demonstrate the feasibility of deploying tuned ChatGPT4All for robotic control tasks. The model, trained on 22,000+ ICRA proceedings papers, achieves 82% similarity to ChatGPT responses while maintaining sub-second inference time. Our evaluation framework combines TF-IDF similarity scoring and LDA topic coherence analysis across several thousand test cases. Results show consistent performance within hardware constraints, with 92% of responses exceeding 0.80 similarity threshold and 98% completing within one second. This study establishes viability of lightweight LLMs for offline mobile robotics applications, providing a foundation for future resource-aware AI deployments.
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| format | Article |
| id | doaj-art-9346dd97e3464092ab8148ae6a7eb6bd |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-9346dd97e3464092ab8148ae6a7eb6bd2025-08-20T02:30:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138943In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking Yegin Genc0Gonca Altuger-Genc1Akin Tatoglu2Pace UniversityFarmingdale State CollegeUniversity of HartfordWe present a novel benchmarking methodology for evaluating offline Large Language Models (LLMs) in resource-constrained mobile robotics applications. Using an Nvidia Jetson Nano platform with 4GB RAM limitation, we demonstrate the feasibility of deploying tuned ChatGPT4All for robotic control tasks. The model, trained on 22,000+ ICRA proceedings papers, achieves 82% similarity to ChatGPT responses while maintaining sub-second inference time. Our evaluation framework combines TF-IDF similarity scoring and LDA topic coherence analysis across several thousand test cases. Results show consistent performance within hardware constraints, with 92% of responses exceeding 0.80 similarity threshold and 98% completing within one second. This study establishes viability of lightweight LLMs for offline mobile robotics applications, providing a foundation for future resource-aware AI deployments. https://journals.flvc.org/FLAIRS/article/view/138943RoboticsOffline LLMResource ConstraintsPerformance BenchmarkingEdge Computing |
| spellingShingle | Yegin Genc Gonca Altuger-Genc Akin Tatoglu In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking Proceedings of the International Florida Artificial Intelligence Research Society Conference Robotics Offline LLM Resource Constraints Performance Benchmarking Edge Computing |
| title | In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking |
| title_full | In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking |
| title_fullStr | In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking |
| title_full_unstemmed | In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking |
| title_short | In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking |
| title_sort | in search of a lightweight good enough offline generative ai for mobile robots performance benchmarking |
| topic | Robotics Offline LLM Resource Constraints Performance Benchmarking Edge Computing |
| url | https://journals.flvc.org/FLAIRS/article/view/138943 |
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