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Inference-first thesis

Psionics is built around a single observation: inference becomes the dominant workload.

When models move from research to production, the constraints shift from “can we train this once?” to “can we serve this reliably, cheaply, and everywhere users are?”

Thesis in 30 seconds

Section titled “Thesis in 30 seconds”
  • Inference dominates: production traffic is continuous and spiky; your infrastructure has to be built for uptime, not demos.
  • Power economics decide cost-per-token: reliable, low-cost power is a first-order constraint because inference is energy-intensive and margin-sensitive.
  • Latency is a product feature: for real-time inference, (p95/p99) latency and network path quality matter as much as GPU count.

What this means for data centers

Section titled “What this means for data centers”

An inference-first facility is engineered for:

  • Predictable performance: stable throughput under bursty production traffic.
  • Operational resilience: commissioning gates, telemetry, runbooks, and clear change control.
  • Network reality: carrier diversity, measurable latency paths, and enough bandwidth headroom to avoid surprise bottlenecks.
  • Density readiness: cooling paths that can evolve with hardware density, without redesign.

How we commercialize it (facilities-first)

Section titled “How we commercialize it (facilities-first)”

We start with:

We define “ready” by commissioning validation, not marketing milestones:

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