09/28/2026
Ásvaettir Labs Update — Local AI, Infrastructure Shift & What’s Coming Next
Hail everyone — quick update.
We’re gearing up for software releases, but there’s no hard production date yet. I’ve been rebuilding our entire environment around local AI models using Bionic, and migrating everything off cloud dependencies for privacy and autonomy.
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Local AI Workstation (Current Build)
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Dell Precision Tower 7810
2× Intel Xeon E5‑2680 v3 (24 cores / 48 threads)
64 GB RAM
2× GTX 1060 (6 GB VRAM each — NOT shared)
1 TB storage
Ubuntu 24.04.4 LTS (GNOME 46, X11)
Kernel 7.0.0‑34
LM Studio — Bionic Runtime
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Models I’m Actively Running
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Qwen3-VL 4B — PROJECT_COORDINATOR
Qwen 14B — ARCHITECT
Qwen2.5-Coder 7B Instruct — SYSTEMS_BUILDER
DeepSeek-Coder-V2-Lite-Instruct — VISUAL_BUILDER
Qwen2.5-VL 3B Instruct — VISION_QA, with Qwen3-VL 4B as the fallback Vision QA model
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The Challenge
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The dual GTX 1060 setup is powerful but tricky:
Each card has its own 6 GB VRAM pool, so larger models need:
CPU/RAM offload
Careful layer distribution
Quantization tuning
Runtime adjustments
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Why Progress Has Been Slow
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Between work, legal schedules, and trying to maintain some normalcy, development has been slower -
but nothing has been abandoned.
I’m currently:
Re‑engineering routers
Rebuilding local model pipelines
Migrating Ásvættir Labs projects to a dedicated offline server
Finalizing cross‑platform builds (Forseti stays Android‑native; other apps are evolving)
This shift is intentional — privacy first, autonomy always.
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Community Question
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If you’re running local AI on similar specs:
Have you hit the same VRAM split issues?
What models or runtimes have worked best for you?
Drop your setup, your struggles, or your wins — I’m genuinely curious what others are doing with mid‑range dual‑GPU rigs.