A full personal-operations stack runs on one 128GB Strix Halo machine, with several named agents reachable through a single Telegram chat and each handling a different part of daily life.
GPU model
Capacity range
Total VRAM
Practice
Model
Quantization
Context length
Reported speed
Runtime
Tools / workflow
Record origin
EXPLORE BY CAPACITY
Start with your machine.
Capacity is the reported total for this setup. Multi-GPU and unified-memory layouts can behave differently.
8 GB or lessLaptops, iGPUs and modest cards9–12 GBEntry-level local inference13–16 GBCurrent mid-range cards17–20 GBRX 7900 XT and compact pro cards21–24 GB3090, 4090 and 7900 XTX class25–32 GB5090 and workstation cards33–48 GBWorkstation and multi-GPU setups49–96 GBLarge multi-GPU and datacenter setups97 GB+Unified memory, servers and clusters
2 resultsEvery record keeps its origin visible.
Personal assistantGenesis archive2026-09
Eight daily agent workflows, driven from Telegram on a Strix HaloCodingGenesis archive2026-09
Building robotics simulators and interactive lessons on a Mac StudioA teacher uses local models to build topic-specific simulators, interactive lectures, and fast examples for students, while sending larger changes to cloud models when more context is needed.