🧠 AI Hub — 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) — 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.

Shipped In build Planned Vision NEW added by the gap analysis / architecture review needs = orange · unlocks = green

Feature details

Tap any feature in the matrix to open its details — 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 · DPO · RFTconversation · instruction-pairs · preference-pairs · graded-tasksServing live · FT Q1'27
Base / completion LLM/v1/completionsContinued pretraining · full-parameter SFTtext-corpusGateway Q3'26 · FT Q2'27
Embedding/v1/embeddingsContrastive fine-tuningcontrastive-tripletsGateway Q3'26 · FT Q1'27
Reranker/v1/rerankPairwise / listwise ranking FTranking-listsGateway Q3'26 · FT Q1'27
Vision LLM/v1/chat/completions (image parts)Multimodal SFTimage-text-pairsServing Q1'27 · FT later

Gap additions mapped into the roadmap

Features added by the gap analysis and the architecture review, now slotted in canvas and matrix alike (marked NEW in the matrix): /v1/rerank → Q3'26 gateway epic · model intake pipeline, platform observability, request/response logging → Q4'26 · dataset foundation, eval harness v0, vision-model serving → Q1'27 · DPO + full-parameter SFT, quantization-aware training, offline distillation → Q2'27 (multi-LoRA moved here too, so it no longer waits at the end of a single-quarter chain) · RFT/RL, inference guardrails 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).