My deep research and understand indicates this project is unrivaled and is an island in on itself, not replacing anything, and complimenting most consumer/smb builds. LexiPanel is my self-hosted control plane for local AI. I built it because I wanted the machine itself to be understandable, measurable and tunable instead of hiding everything behind presets. Yes, it was heavily vibe-coded, it’s named after my kid “Panel,” and I built it for my own homelab first. The core idea now is simple: fit AI to the hardware and workload, don’t just launch it. What it does now: Runs multiple independent llama.cpp, stable-diffusion.cpp, audio.cpp, Camelid and ONNX Runtime instances from one browser CPU/GPU/NPU support, including ONNX paths for AMD Ryzen AI, Intel and Qualcomm NPUs 220+ explained controls, plus passthrough access to flags exposed by the active llama.cpp build Shows exact launch command/env, warnings, VRAM/RAM estimates and refusal reasons before start Reads GGUF metadata, accounts for already-resident workloads and prevents unsafe launches Measures long-context decode behavior instead of treating one tok/s number as the whole story Benchmarks coding and agent workloads and compares configs/models against the workload you actually run Auto-fit learns idle windows, tests safe changes, checks them against later real traffic and rolls back regressions Fit can benchmark quant formats on your actual cards, create tensor-level requant plans to a real VRAM budget, build them and verify the result GPU tuning measures speed, thermals, power and tokens/joule. Supported AMD tuning can auto-revert unstable settings Power profiles cover CPU, PCIe/NVMe, GPU caps/fans, watchdog behavior and PSU/UPS budgeting OpenAI-compatible gateway with users, API keys, quotas, model restrictions and usage accounting Fleet mode: multiple LexiPanel boxes report into one primary, and running models can be shared through one gateway with basic replica selection/failover 28 MCP tools for status, models, launch plans, benchmarks, optimization, power/GPU state, diagnostics and more Hermes Agent compatibility/config generation Built-in llama.cpp Web UI integration Resumable HF downloads, engine build management, crash forensics, diagnostics, file manager, web terminal and backups Graph Gauntlet is still there because staring at charts gets old The backend is still deliberately boring: Python stdlib only, no pip application deps, no Docker, no database, no frontend build system. State is files + systemd. The part I think is different is the loop: discover → fit → optimize → validate → operate → learn → adapt It’s not trying to replace Open WebUI, Ollama, GPUStack, LocalAI, vLLM, etc. The goal is to sit underneath apps and agents and make a local AI box, or a small mismatched fleet, run as well, safely and transparently as the hardware allows. Still refining it. Constructive criticism, edge cases and good ideas are very welcome. https://github.com/W61k3r/LexiPanel   submitted by   /u/W61k3r [link]   [comments]