Jetson Nano runs Ollama with Q4_K_M quantization for

Short summary

Running Llama models on Jetson Nano requires aggressive quantization: switching from Q8_0 to Q4_K_M achieved 25x speedup but introduced reliability issues, solved with automatic retry logic. The author documents building Ollama with CUDA support and benchmarks different quantization strategies with real performance data. Includes practical installation steps for deploying local AI inference on edge hardware with limited resources.

• •Q4_K_M quantization achieves 25x speedup vs Q8_0 on Jetson Nano's GPU
• •Lower precision trades accuracy for speed: 50-65% error rate, mitigated by automatic retries
• •Step-by-step guide for building Ollama from source with CUDA support on edge hardware

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