The system is based on an AMD Ryzen AI Max+ 395 class machine with 128GB unified memory and ROCm-oriented local inference. That makes it different from a conventional desktop GPU box: it can attempt models and contexts that are awkward or impossible on smaller discrete cards, even when those cards are faster.

01 – The Workstation Stack

The v2 archive recovers a July 2026 layer of the open-source workstation stack:

• local inference and chat;
• coding agents;
• generative media;
• voice.

The point was not to install one impressive demo. It was to assemble a workstation where local models, coding tools, ComfyUI-style media workflows and voice systems could coexist.

02 – Hilbert Unofficial Suite

The Hilbert Unofficial Suite combined several layers:

• Hilbert Studio, a ComfyUI workflow platform;
• Genesis Runtime, an assistant/agent runtime;
• an AI code-build pipeline;
• ai-switch, a switcher intended to keep GPU-heavy stacks from trampling each other.

The suite's workflow layer was manifest-driven: declared inputs and outputs, model requirements, hardware requirements, tags, presets, registry discovery, schema validation, batch processing and quality-control scoring.

At one point the public registry was recorded as 12 categories and 89 workflows. Adult and community-model checkpoint categories were intentionally excluded from the public registry.

03 – ROCm As A Constraint

The ROCm focus is not incidental.

Most generative-AI tooling assumes NVIDIA/CUDA by default. Hilbert made ROCm and unified memory first-class constraints, which is exactly the kind of constraint the Lab should document: the engineering work required to make AI practical on the hardware actually available.

04 – Failures Count

The Hilbert archive includes operational failures:

• ROCm/unified-memory messages involving Qwen3 Coder Next and SVM allocation;
• sleep/resume instability after moving to Pop!_OS;
• a disk-capacity incident where Code state and the AI suite consumed enormous storage.

Those are not footnotes. They are part of the research output.

05 – Relationship To The GPU Farm

Hilbert and the older GPU farm solve different problems:

• Hilbert provides capacity through unified memory;
• discrete GPUs provide speed and throughput for narrower specialised tasks.

That distinction feeds directly into Omega's cognitive-cluster direction.

Source note: assembled from v2 sections on Hilbert, the recovered July 2026 workstation layer and the Hilbert Unofficial Suite.

Sometimes the right platform is a strange pile of old cards, cheap boards, risers, unified memory and careful workload assignment.

01 – The Hardware Pattern

The archive records experiments or plans around:

• Tesla P40;
• Tesla P4;
• planned P100s;
• Radeon RX 580;
• Radeon RX 7970 6GB;
• RX 5700 XT;
• RTX 4060;
• RTX 4070;
• Hilbert unified memory;
• ASRock mining boards;
• Supermicro dual-Opteron server hardware.

The recurring question is not "what is the fastest single GPU?" It is "what useful cognitive or generative role can this hardware perform?"

02 – P40 Logic

The Tesla P40 is interesting because 24GB of VRAM can matter more than modern gaming performance for some local LLM experiments.

The trade-off is explicit:

• older card;
• more memory per dollar;
• lower modern feature support;
• awkward cooling and power;
• useful capacity for certain inference roles.

That is not nostalgia. It is budget-aware systems design.

03 – Dedicated Function Nodes

The mature direction is not necessarily one model spread across every card.

Instead, cards can be assigned to roles:

• one GPU for video generation;
• one for image generation;
• one for an LLM;
• smaller cards for lightweight specialist processors;
• Hilbert for large capacity-bound models;
• CPU services for routing and state.

This maps cleanly onto Omega's specialist cognitive-cluster idea.

04 – PCIe x1 Is Not Automatically Disqualifying

Mining boards and risers are poor for workloads requiring heavy inter-GPU transfer. But for dedicated-function nodes, PCIe x1 can still be acceptable if the model loads and does substantial local work on the card.

That distinction is important. The Lab should not pretend cheap mining-era hardware is secretly ideal. It should document where it is bad, and where it is still useful.

05 – Relic And Scrap LLM

The r/RelicAndScrapLLM idea belongs here because it names the philosophy directly: old hardware can still be part of serious AI experimentation if the workload is chosen honestly.

The point is not to beat flagship GPUs. The point is to expand what can be learned with the machines already within reach.

Source note: assembled from v2 hardware sections and the recovered research philosophy around capacity, throughput and reuse.