ON1 (G116 V8): 38μs Black-Box AI Memory Retrieval on Virtual Chip ISA

ON1 (G116 V8) has introduced a virtual chip ISA that achieves 38-microsecond black-box AI memory retrieval by separating vector search into three observable latency stages: fetch, compute, and ANN search. The system, designed for real-time LLM grounding with llama.cpp, exposes memory, compute, and retrieval latencies individually rather than reporting a single opaque query time. A public verification endpoint is currently live for testing the latency decomposition.

G116 v8: 38μs Black-box AI Memory Retrieval on Virtual Chip ISA Latency-Separated Fetch/Compute/ANN — Live Tunnel Inside Unlike any conventional chip.G116 v8 introduces aquantum-inspired virtual ISAthat makes memory, compute, and ANN search latency observable – not just a single opaque query time. Built for the next generation of LLMs llama.cpp, real‑time RAG, natural language grounding . G116 v8 decomposes vector retrieval into three hardware‑visible stages, just like a quantum memory fabric: Fetch Layer – mmap‑based dataset mapping zero‑copy, ~0.1–0.5 μs/op Compute Layer – vector transformations NumPy / BLAS, ~0.4–2 μs/op Search Layer – ANN similarity currently brute‑force, ~3–10 ms/op; FAISS/HNSW coming This is not another black‑box vector DB. It’s a virtual chip ISA that makes RAG bottlenecks transparent. | Tier | Latency per op | |---|---| | Fetch | 0.1 – 0.5 μs | | Compute | 0.4 – 2.0 μs | | Search brute | 3 – 10 ms | Next: FAISS indexing + GPU acceleration Most systems FAISS / Milvus / pgvector only give you: “query latency = X ms” We give you: memory latency → compute latency → retrieval latency This is the natural language language latency breakdown needed for real‑time LLM grounding with llama.cpp. Our public verification endpoint is currently live. You can test the latency decomposition directly from your own terminal right now: curl " https://5e776b15817fd1.lhr.life/query?mode=search&n=5000&k=3 https://5e776b15817fd1.lhr.life/query?mode=search&n=5000&k=3 "