Embedded systems • CUDA optimization • Edge AI
Breaking New Ground: JetPack 7.2 Orin Nano Custom OS Deployment
Deploy CUDA-accelerated LLM inference on newly-supported Jetson Orin Nano 8GB (JetPack 7.2 first-time support) with minimal OS footprint. The platform presented unprecedented firmware and kernel stability challenges—three critical defects completely invisible to standard dependency resolution or documentation:
Pioneering JetPack 7.2 Research on Newly-Supported Orin Nano Platform
Built minimal Yocto OS targeting JetPack 7.2/L4T R39.2—the first LTS release to officially support Orin Nano. Applied novel research techniques to surface and resolve three critical firmware/kernel incompatibilities invisible to standard dependency resolution:
tcu_muxer) to trace timing-sensitive race condition in UEFI early boot; bypassed defective minimal UEFI path by integrating custom rootfs into proven official flashing mechanismstrace/lsmod capture against reference system to establish ground-truth kernel module dependencies (~67MB transitive closure)MACHINE to jetson-orin-nano-devkit-nvme with explicit overrides for SKU-specific boot chainsRequires= blocking clock-locking on this board's kernel config; relaxed to Wants= + After= ordering for automatic 1.728GHz CPU lock on every bootProduction-ready JetPack 7.2 minimal OS achieving breakthrough performance on first-generation Orin Nano support with fully functional CUDA/TensorRT stack
Research Impact: Validated TensorRT-Edge-LLM on custom minimal OS matching stock JetPack inference parity. Identified critical systemd dependency bugs in NVIDIA's vendor-supplied services; demonstrated dynamic clock scaling superior to locked-clock configurations for power efficiency (3.23 vs 2.82 tok/s/W). Established reference baseline for embedded LLM deployment on newly-supported hardware.
Research-level expertise spanning firmware, kernel, and inference stack optimization:
Hardware & Firmware: Hardware UART demultiplexing (tcu_muxer) • UEFI early-boot race condition debugging • QSPI bootloader SKU specialization • Non-deterministic failure isolation • Controlled A/B testing methodology
Kernel & ABI: Kernel module ABI incompatibility discovery • Transitive dependency closure computation (~67MB curated kernel) • CUDA runtime initialization debugging (error 801 resolution) • Strace/lsmod empirical dependency mapping vs. documentation inference
Systems & Optimization: Yocto BSP customization & meta-tegra integration • Systemd service dependency graph analysis & relaxation • Performance profiling (prefill/decode throughput) • Thermal tuning & CPU/GPU clock management • Power efficiency research (dynamic vs. locked-clock comparison)
AI Inference Stack: TensorRT-Edge-LLM integration & validation • CUDA unified-memory budget optimization for 4B models • Token generation throughput benchmarking • On-device agentic coding pipeline enablement
Technical findings from production edge AI deployment
Deploying Qwen2-VL-2B on Jetson Orin Nano: unified-memory OOM patterns, CUDA 13.2 SBSA unification enabling upstream wheels, and the pipeline architecture for near-real-time captioning on 8GB devices.
YOLOv8n inference on Orin: 1.7x speedup over PyTorch (30ms → 17ms), kernel autotuning for compute capability 8.7, achieving 57 FPS object detection and identifying bottlenecks in the capture→encode→serve pipeline.
Running VLM + TensorRT detector simultaneously on 8GB: process consolidation, int8 vs int4 quantization kernel compatibility on recent hardware, and the memory-accounting gap between "model size" and "process RSS" under concurrent GPU workloads.
Current research & testing on additional SBC platforms with similar architectures
RK3588 SoC with 6 TOPS NPU (INT8). Optimizing llama.cpp + rkllm-toolkit for portable agent deployment; NPU acceleration benchmarking on 7B-class models; power efficiency vs Orin Nano comparison.
RK3588 hardware substitution validation. Testing cost-effective alternative to ROCK 5B+; verifying rkllm-toolkit / llama.cpp portability; GPIO/peripheral compatibility for agent ecosystems.
Nvidia Jetson Orin NX 16GB on Seeed carrier. Benchmarking INT4/INT8 quantized inference (7B-class models); measuring thermal throttling under sustained load; validating llama.cpp decode latency parity with official Jetson carriers.
Parameter-efficient fine-tuning of open-weight models on domain-specific datasets. LoRA and 4-bit QLoRA adapter training, dataset curation for supervised fine-tuning, and adapter merging for deployment — full write-up with metrics coming soon.
Deploying quantization strategies (INT4/INT8) across RK3588 NPU + Orin CUDA. Comparative inference latency, power draw, accuracy trade-offs. Publishing optimization baseline for edge AI practitioners.
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