// SKUNKWORKS_RESEARCH_HUB

In 2026, the biggest constraint for AI in South Africa isn’t compute—it’s kilowatts. While the global North talks about parameter counts, South African founders are talking about Inverter-to-GPU ratios.

The Adoption Gap

94% of SA businesses want AI integration, but "Proof-of-Concept Fatigue" is real. Companies are tired of black-box APIs that send sensitive data to the US and fail during load-shedding.

The ReactAI Take

Adoption in SA requires Sovereign Intelligence. By hosting our "Neural Core" at Teraco and running our R&D hub on a 100% solar-redundant Skunkworks lab, we bypass the grid instability. For a South African enterprise, a "Smart" system is only smart if it's Online. We aren't just building AI; we are building infrastructure that survives the local reality.

You don't need an H100 cluster to produce world-class synthetic media. In this log, we break down how we run FLUX.1 [dev] on 8GB and 12GB VRAM cards without the "Out of Memory" (OOM) death loop.

The Stack:

• Quantization is King: We’ve shifted entirely to 4-bit and 8-bit GGUF/NF4 formats. This reduces memory footprint by ~70% with negligible loss in prompt adherence.
• T5 Offloading: By loading the T5 text encoder into System RAM instead of VRAM, we save crucial gigabytes for the actual diffusion process.
• The "Clean Cache" Routine: Our custom ComfyUI nodes now trigger a torch.cuda.empty_cache() after every 5th generation to prevent fragmented memory build-up.

Result: We’ve achieved sub-60s generation times on mid-range gaming laptops—proving that our local models can scale anywhere, from our Sandton hub to a laptop in Tokyo.

Digitizing legacy archives isn't just about scanning; it's about making history queryable. When dealing with 30-year-old physical records, the "Uncanny Valley" isn't visual—it's structural.

The Technical Challenge

Low-contrast ink and thermal paper degradation cause standard OCR engines to fail. If a digit is misread in a legal contract, the entire Neural Core loses its reliability.

Our Skunkworks Solution:

• Multi-Model Consensus: We run three separate OCR architectures (Tesseract, PaddleOCR, and a custom Vision-LLM) then use a voting algorithm to determine the highest-confidence character.
• Denoiser Pre-processing: Before ingestion, we run a diffusion-based noise reduction pass that "restores" faded ink signatures while ignoring paper texture.
• Vector Alignment: We found that embedding archival data with overlapping window fragments (1024 tokens with 256 overlap) provides the most stable retrieval for RAG-based legal search.