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)"