Start With the Operating Platform
The documented platform uses Ubuntu Server LTS on an AMD Ryzen 7 5800XT system with 128 GB of DDR4 memory and an NVIDIA RTX 5070 Ti. The server operates headlessly through SSH and remote management tools. Establishing remote access and a stable network comes before adding model workloads.
Validate the GPU Before the Model
The project notes record NVIDIA driver configuration and verification that the RTX 5070 Ti is available to local AI workloads. Keeping driver, runtime, and application checks separate makes a failure easier to locate. Hardware availability does not, by itself, establish model performance.
Separate the Service Layers
Ollama provides local model hosting, while Docker and Open WebUI are part of the service environment. The useful troubleshooting question is which layer is failing: the host, the GPU stack, the container, the model runtime, or the client connection. Documenting those dependencies is more useful than treating the stack as one application.
Operate Before Expanding
Remote administration is established in the documented build. Expanded logging, GPU utilization tracking, service-health visibility, and security automation remain areas for further development. The next evidence should show a repeatable service check and a recovery path, not simply another installed tool.
What a Reproducible Benchmark Needs
Future LLM inference benchmarks should record the model, quantization, VRAM usage, prompt and output lengths, warm-up conditions, concurrent requests, software versions, hardware, and measurement method. Quantization and VRAM management are topics for further investigation; measured inference performance has not yet been published.
Takeaway
Treat local AI as infrastructure: verify each layer, keep the dependencies visible, and publish measurements with enough context for someone else to reproduce them.
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