Engineering Control Plane for AI Infrastructure
Lens, Pulse, Relay, Atlas, Oracle, and Forge are capabilities inside one platform — not separate tools you stitch together. Each module serves a phase of the infrastructure decision workflow.
AI Infrastructure Intelligence
Predict infrastructure cost, performance, bottlenecks, and capacity before deployment. Roofline-based models over real GPU, model, and fabric catalogs — with explainable recommendations.
Inference Benchmarking & Telemetry
Benchmark LLM inference workloads and visualize TTFT, ITL, TPOT, end-to-end latency, throughput, and GPU telemetry across public or custom datasets.
Self-hosted open-weight serving
Download open-weight models, serve them on your own Mac with llama.cpp (or vLLM on Linux NVIDIA), and compare two local endpoints side by side — TTFT, stream speed, and output.
Your private ChatGPT — on your Mac
Host a ChatGPT-like app entirely on your machine. Small 1–3B open models via llama.cpp, multi-turn chat, zero cloud. Fine-tune and publish your own models through Relay later.
Natural extensions of the same workflow — not separate product lines.
XARIV Atlas
Infrastructure Knowledge Graph
Continuously calibrated infrastructure intelligence from telemetry, benchmarks, and production deployments. Coming soon.
XARIV Oracle
Capacity Planning
Estimate GPU fleet size, utilization curves, and infrastructure cost as traffic, models, and hardware change — grounded in workload profiles, not spreadsheets.
XARIV Forge
Infrastructure Simulation
A digital twin for AI infrastructure. Mirror a cluster and simulate new models, traffic growth, MoE routing, and agent workloads to predict bottlenecks before they happen.