FanFrame is a manual per-fan control application with live temperature and RPM readouts for the Framework Desktop (AMD Ryzen AI MAX) on Linux. Developed by community member iovis801, it provides a GTK4 GUI with per-fan duty sliders backed by a minimal privileged root daemon that communicates with Framework's official framework_tool CLI.
The Framework Desktop is a Mini-ITX workstation announced at the 2nd Gen event in February 2025. It features an AMD Strix Halo APU and is designed to be modular, containing two front-side expansion card slots as seen in Framework's laptop models. The front panel has slots for 21 interchangeable tiles for user customization. Pre-orders opened February 25, 2025 with shipping planned for early Q3 2025. After missing that target by ~6 weeks, Batch 1 units shipped the week of August 11, 2025 alongside the review embargo lift. Memory is soldered LPDDR5x (non-upgradeable) — Framework worked with AMD for months but concluded it was not technically feasible to land modular memory at high throughput with the 256-bit memory bus.
Summary
framework-rocm is a set of reproducible ROCm containers for the Framework Desktop (AMD Ryzen AI MAX / Strix Halo / Radeon 8060S, gfx1151). Developed by community member geoff-davis, it provides pinned PyTorch and JAX Docker images with GPU smoke tests and measured attention/training performance findings.
Strix Halo Guide is a comprehensive, practical guide to running large language models locally on AMD Strix Halo / Ryzen AI MAX+ 395 systems with Radeon 8060S (gfx1151) and 96GB/128GB unified memory. Maintained by community member hogeheer499-commits, it covers BIOS configuration, Ubuntu 24.04/kernel setup, Ollama, llama.cpp Vulkan/RADV, ROCm/HIP experiments, vLLM, benchmarks, raw logs, and reproducibility checks.
strix-llm is a known-good local LLM inference configuration tool for the Framework Desktop (Ryzen AI MAX+ 395 / Strix Halo). Maintained by community member Alberto Migliorato (GitHub: AlbeMiglio), it provides a tested, reproducible setup for running large language models locally on Framework Desktop hardware using ROCm and llama.cpp.