01 / GPU PIPELINE
Inference that respects the accelerator.
Designed around TensorRT-LLM, NIM, Triton, and CUDA-X for FP8/INT4 post-training and high-concurrency agent reasoning across Hopper and Blackwell systems.
Proprietary AI runtime & distributed substrate architecture
Deterministic agentic infrastructure from edge to accelerated cloud.
Ninth Substrate engineers high-throughput neural execution runtimes, low-latency vector substrates, and hardware-accelerated inference pipelines designed for mission-critical autonomous systems.
SYSTEMS NOMINAL // US-MIDWEST & GLOBAL EDGE
Runtime topology / simulated telemetry LIVE
L0
Physical & RF / Edge Sensing
ingest channel / deterministic path
L1
Local Deterministic Micro-Runtime
compute channel / deterministic path
L2
High-Dimensional Vector State
state channel / deterministic path
L3
Multi-Agent Cognitive Orchestration
agents channel / deterministic path
L4
Distributed GPU Cluster & Cloud Mesh
accelerate channel / deterministic path
Throughput
8.42k evt/s
P99 path
83.1 ms
State sync
NOMINAL
DEMO DATA // VALUES ARE SIMULATED FOR PRODUCT EXPLORATION
Architecture / substrate topology
A deterministic path for stateful autonomous systems.
The substrate coordinates sensing, local execution, vector state, agent reasoning, and accelerated inference without forcing every decision through a distant control plane.
Engineered for accelerated compute & global edge
Portable execution across the hardware layers that matter.
02 / EDGE PLANE
State close to the decision.
Workers, R2, Vectorize, and Durable Objects form a zero-trust data plane for low-latency global state synchronization and controlled egress.
03 / HYBRID MESH
No single-cloud assumption.
Containerized workloads can move across EKS, GKE, AKS, and bare-metal GPU nodes while preserving a consistent substrate contract.
Benchmark matrix / simulated
Explore the performance envelope.
These values are interface demonstrations, not production claims. Replace them with measured hardware data before external use.
Simulated benchmark profile
Multi-Agent Swarm Coordination
TTFT
182 ms
Tokens / sec / watt
46.8
P99 latency
640 ms
Memory reduction
38%
SIMULATED VALUES ONLY // REPLACE WITH MEASURED HARDWARE BENCHMARKS BEFORE EXTERNAL CLAIMS
Product roadmap / active R&D
From substrate prototype to autonomous runtime.
Q4 2026 / CURRENT
Core substrate prototype
Deterministic local runtime, vector/state primitives, and edge-to-cloud coordination surface.
Q1–Q2 2027 / NEXT
Distributed NIM & TensorRT-LLM benchmarking
Measured accelerator profiles, quantization tradeoffs, and high-concurrency orchestration tests.
2027 / TARGET
Enterprise autonomous runtime GA
Hardened deployment contracts for mission-critical partner environments and design partners.
Institutional & partner intake
Open a technical conversation.
For investors, hardware/cloud partners, and enterprise design partners evaluating the substrate architecture.