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Private AI infrastructure

TensorBreeze

Every GPU you own. One private AI endpoint.

A self-hosted GPU control plane for heterogeneous Windows and Linux machines, with bounded workload routing, fleet telemetry, reservations, and topology-aware orchestration.

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TensorBreeze preview reading Every GPU you own, one private AI endpoint

Public, sanitized project material.

01

The problem

Why it exists

Useful GPUs often sit in machines with different operating systems, availability windows, trust levels, and failure modes.

Applications should not need to know which machine owns a device—or become unstable because a remote worker changes underneath them.

Limits
  • TensorBreeze is currently private while its architecture matures, with an open-source release planned.
  • Public diagrams omit real machine names, addresses, credentials, and deployment topology.
02

Architecture

Operating model

Real-world proof

A large model was loaded and executed on a remote RTX 5090 while the originating application remained on another machine.

03

Capabilities

What it does

01One private endpoint for a GPU fleet
02Outbound-only workers
03GPU telemetry and reservations
04Bounded model and workload control
05Application-scoped routing
06Heterogeneous fleet awareness
04

Security

Trust model

  • Workers initiate outbound connections and expose no general remote shell.
  • Read-only telemetry, capacity reservation, and workload execution are separate switches.
  • Application credentials select allowlisted models—not nodes, ports, commands, paths, or provider secrets.
05

Tradeoffs

Key decisions

Stable applications, disposable workers

Applications stay on the reliable side of the boundary while GPU workers can be restarted, upgraded, or taken offline independently.

Capability-specific trust

Telemetry, queueing, routing, and execution do not share one oversized credential or one all-powerful process.

06

Current state

Current state

Implemented
  • Controller, Worker, Runner, Router, and Tunnel boundaries
  • Windows, WSL2, and native Linux worker support
  • Read-only telemetry with separately gated controls
  • OpenAI-compatible application routing
In development / planned
  • Broader application marketplace
  • Deeper topology-aware scheduling
  • Open-source release once the architecture and boundaries are ready
07

Lessons

What the work clarified

  1. Heterogeneous compute is primarily a policy problem, not a device-discovery problem.
  2. A useful control plane makes absence and degradation first-class states.
  3. The safest remote control is a small, typed contract—not a shell with good intentions.