NewCo360 AI Infrastructure

AI infrastructure

Distributed inference with hybrid GPU coordination

Peer-to-peer inference architecture designed to reduce serving cost while preserving usable latency.

90% infra cost reduction

~370ms TTFT

69+ tokens/sec

This initiative focused on distributed inference across available hardware with an interface that could remain compatible with existing application integrations.

The challenge was balancing cost, memory pressure, and response quality without forcing product teams to rewrite their client layer.

What was built

  • peer-to-peer coordination for inference workloads
  • serving path compatible with OpenAI-style APIs
  • memory and latency optimization across worker nodes
  • benchmark-driven tuning for throughput and time-to-first-token

Why it mattered

For organizations evaluating private AI or hybrid serving, infrastructure cost often becomes the constraint before adoption does. This case shows how distributed systems design can materially change the economics of AI delivery.