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Simple Benchmark Review: Ollama on Jetson Nano
TL;DR: A maker documents benchmarking Ollama models on a Jetson Nano, exploring how quantization and resource constraints affect performance and accuracy for a local, free AI app that generates flashcards. The post emphasizes that benchmark results are highly use-case dependent and highlights practical challenges like RAM limits and swap files.
The author shares a hands-on journey benchmarking AI models on Jetson Nano using Ollama, testing different quantizations to find a balance between performance and stability. They note RAM shortages necessitated a swap file, and used a simple OSI quiz as a ground-truth reference to compare results. The key takeaway is that heavy quantization reduces quality, and benchmarks can only be meaningful for specific use cases. Acknowledge that mapping performance across many scenarios is lengthy and nuanced.
Question for the room: What has been your approach to benchmarking AI models on resource-constrained devices, and how do you decide which metrics matter most for your use case?
— via dev.to
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