140,000× management overhead reduction. 343.9µs median decision cycles. The software ceiling has been removed — we are limited only by the speed of light in copper.
Three architectural phases eliminated every software bottleneck. What remains is pure hardware throughput — PCIe bandwidth and GPU compute.
Total management overhead reduced from 14,605µs to 0.13µs. The system is now hardware-limited.
Full transparency on what scales O(1) versus O(N). Management overhead is constant. Data movement is bandwidth-limited. No hidden complexity.
| Operation | N=8 | N=100K | Growth | Class |
|---|---|---|---|---|
| Epoch Reset | 0.02µs | 0.03µs | O(1) | |
| Registry Mapping | 0.08µs | 0.10µs | O(1) | |
| Regime Similarity | 1,755µs | 1,789µs | O(1) | |
| WorldIndex Query | 33.6µs | 33.6µs | O(1) | |
| DMA Data Transfer | 191µs | 3,247µs | O(N) | |
| Consensus Kernel | 263µs | 739µs | O(N) |
WorldIndex achieves mathematically proven O(1) latency. The GPU Council is bandwidth-limited at high N but sub-350µs at production scale (N≤256).
2,000 consecutive stress cycles on NVIDIA RTX PRO 6000 Blackwell. Production-scale determinism validated with percentile-level granularity.
"Remaining P99.9 tail spikes are OS kernel interrupts and PCIe bus arbitration — not the algorithm. The system achieves near-deterministic execution, limited only by hardware physics."
Every number on this page is reproducible with a single command. No cherry-picked benchmarks. No asterisks. Full measurement methodology provided.
# Full benchmark with scaling analysis + jitter profiling $ NEURALCHAT_BACKEND=gpu CUPY_COMPILE_WITH_PTX=1 \ python src/neural_chat/bench_gcs.py # Output (N=256, production scale): begin_cycle_fast (registry): 0.08 µs ✅ O(1) votes BULK (1 pinned DMA): 201.08 µs (3.2×) consensus kernel: 341.37 µs E2E BULK (pinned DMA): 326.19 µs (<1ms ✅ 2.7×) # Jitter analysis (2,000 consecutive cycles): P50 (median): 343.9 µs Sub-500µs: 91.1% P95: 585.3 µs Sub-1ms: 98.8% P99: 1,301.9 µs
All benchmarks executed on production-grade workstation hardware.
GPU Council performance is half the story. The perception engines must also pass. Five domains. 42 tests. Every claim backed by reproducible artifacts.
# Robotics proof suite (Apex17 spatial engine) $ g++ -std=c++20 -O2 tests/test_spatial_prior.cpp -o test && ./test [PASS] voxel_downsample 1M→65K pts ✅ [PASS] persistence_h0 23 components ✅ [PASS] fingerprint_determinism sim = 1.000 ✅ [PASS] scene_memory_o1 O(1) recall ✅ # Healthcare proof suite (Apex17 clinical engine) $ python proof-artifacts/benchmarks/run_clinical_proof.py [PASS] ct_voxel_segmentation 24,076 tissue pts ✅ [PASS] ecg_topology 12 beats · RR=833ms ✅ [PASS] vitals_trajectory det > stable ✅ [PASS] council_consensus Intervene · 100% ✅ [PASS] fingerprint_determinism 0xE8DE22FC ✅ [PASS] latency_gate 0.4ms < 100ms ✅ # Defense ISR proof suite (Apex17 ISR engine) $ python proof-artifacts/benchmarks/run_isr_proof.py [PASS] sar_tile_extraction 18,432 reflectivity pts ✅ [PASS] pdw_pulse_topology 8 emitters detected ✅ [PASS] persistence_h0 11 components ✅ [PASS] emitter_fingerprint O(1) recall ✅ [PASS] multi_int_council Suspect · 87% ✅ [PASS] threat_classification Level2-Suspect ✅ # Cyber proof suite (Apex17 cyber engine) $ python proof-artifacts/benchmarks/run_cyber_proof.py [PASS] netflow_extraction 500 traffic pts ✅ [PASS] dns_query_graph 9 DGA candidates ✅ [PASS] endpoint_process_tree 4 suspicious procs ✅ [PASS] persistence_h0 12 components ✅ [PASS] threat_fingerprint O(1) recall ✅ [PASS] soc_council Suspicious · 83% ✅ ───────────────────────────────────────────── Domains Proven: [Markets, Robotics, Healthcare, Defense, Cyber] Total Tests: 42 / 42 (100%)
The identical chain-graph Union-Find algorithm that powers Apex17 robotics, rewritten for market data. Price bars replace LiDAR points. Temporal adjacency replaces spatial adjacency. Regime hashes replace scene fingerprints.
| Capability | H₀ Topology | Traditional TA | ML Regime Detection |
|---|---|---|---|
| Detection Latency | <1ms | ~50ms | ~200ms |
| Regime Recall | O(1) SHA-256 | N/A | Retrain required |
| Labeled Regimes | 44 | ~3 (bull/bear/flat) | Model-dependent |
| Deterministic | ✓ | ✓ | ✗ |
| Adapts Without Retraining | ✓ | ✗ | ✗ |
| Structural Identity | Birth-death barcode | Moving averages | Latent features |
Source: src/neural_chat/market_topology.py · 318 lines · Pure Python H₀ persistence
Each regime gets a 20-dimensional fingerprint vector capturing spectral, volatility, flow, and momentum signatures. Time-decay cosine similarity recall finds "I've seen this exact market before" in O(1).
High-Win Zone regimes (80-100% win rate) trigger aggressive positioning. Bearish Slide and Low-Win Danger regimes trigger veto or cautious downsizing.
The full observe → fingerprint → recall → policy → execute → record chain is live. Regime memory adjusts position sizing in real-time. Risk gates enforce 8 configurable limits before every order.
Source: src/neural_chat/market_topology.py + regime_memory.py + titan_risk_agent.py
Raw output from a Titan V5 GPU Council benchmark run (N=256). Every number is deterministic — run it yourself on RTX PRO 6000 Blackwell.
python proof-artifacts/benchmarks/run_gpu_benchmark.py --n=256 — same output every run
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