GitHub - ON1-Hao/ON1: G116 v8: 38μs Black-box AI Memory Retrieval on Virtual Chip ISA (Latency-Separated Fetch/Compute/ANN) — Live Tunnel Inside · GitHub
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ON1
G116 v8: 38μs Black-box AI Memory Retrieval on Virtual Chip ISA (Latency-Separated Fetch/Compute/ANN) — Live Tunnel Inside
G116 v8: Quantum-Inspired Virtual Memory Chip – A New Paradigm for Black-Box AI Retrieval
Unlike any conventional chip.<br>G116 v8 introduces a quantum-inspired virtual ISA that 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).
System Overview (Latency‑Separated Tiers)
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.
Benchmark (CPU, 10k–100k vectors)
Tier<br>Latency (per op)
Fetch<br>0.1 – 0.5 μs
Compute<br>0.4 – 2.0 μs
Search (brute)<br>3 – 10 ms
(Next: FAISS indexing + GPU acceleration)
The “Quantum” Difference
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.
Public Test Endpoint (Bare‑Metal Tunnel Live)
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)"
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G116 v8: 38μs Black-box AI Memory Retrieval on Virtual Chip ISA (Latency-Separated Fetch/Compute/ANN) — Live Tunnel Inside
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