🧠 AI Hub β€” Roadmap Dependency Matrix

BlackForge AI infrastructure substrate. Rows are features ordered by dependency inside each workstream; columns are quarters; a chip marks the quarter a feature ships. Hover or tap a row to trace what it needs (upstream) and what it unlocks (downstream) β€” tapping also opens its implementation details in a panel. Click a quarter header to show only that quarter's features; click it again to restore.

βœ“Shipped ●In build β—ŒPlanned β—‡Vision NEW added vs. current canvas (gap analysis) needs = orange Β· unlocks = green

Feature details

Tap any feature in the matrix to open its details β€” how it fits the current stack (bf-* services, DGX fleet) and open-source options to adapt or embed. Printing this page includes every feature's details expanded.

Other notes

Model-class taxonomy

Model class is a first-class registry attribute: it determines the served endpoint, the applicable fine-tuning methods, and the dataset types those methods require. Embedding + reranker + chat compose the RAG stack consumed by Nexus RAG.

Model classEndpointFine-tuning methodsDataset typesArrives
Chat / instruct LLM/v1/chat/completionsLoRA/QLoRA SFT Β· DPO Β· RFTconversation Β· instruction-pairs Β· preference-pairs Β· graded-tasksServing live Β· FT Q1'27
Base / completion LLM/v1/completionsContinued pretraining Β· full-parameter SFTtext-corpusGateway Q3'26 Β· FT Q2'27
Embedding/v1/embeddingsContrastive fine-tuningcontrastive-tripletsGateway Q3'26 Β· FT Q1'27
Reranker/v1/rerankPairwise / listwise ranking FTranking-listsGateway Q3'26 Β· FT Q1'27
Vision LLM/v1/chat/completions (image parts)Multimodal SFTimage-text-pairsAfter gateway GA β€” not yet scheduled

Gap additions mapped into the roadmap

Features the commercial reference platform offers that were missing from the canvas, now slotted (marked NEW in the matrix): /v1/rerank β†’ Q3'26 gateway epic Β· generic dataset foundation + eval harness v0 β†’ Q1'27 (eval harness pulled forward β€” DPO, distillation and RFT are only trustworthy with measurement) Β· DPO + full-parameter SFT, quantization-aware training, offline distillation β†’ Q2'27 Β· RFT/RL and fine-tune-from-traffic stay vision.
Deliberately out of scope: audio transcription, image generation, disaggregated prefill/decode, trillion-parameter MoE serving. Differentiator kept: sovereign air-gapped bundle (Q2'27).