vLLM
Signed model services behind the Router, offline at runtime.
Serving todayPrivate AI infrastructure
Every GPU you own. One private AI endpoint.
TensorBreeze turns the GPU machines you already own — Windows or Linux — into one OpenAI-compatible endpoint on your own network. No port forwarding. No Kubernetes.
- Workers
- Linux + Windows (WSL2)
- Enrollment
- Outbound-only, no open ports
- Application API
- OpenAI-compatible
- Status
- Early access
- Base URL
- http://127.0.0.1:8321/v1
- API key
- bra_••••••••••••••••
- Model
- your-model-alias
routed → RTX 5090 · windows-tower · WSL2
Three settings. No TensorBreeze-specific code.
One fleet, many machines
Schedule the work. Breeze handles the hardware.
Applications talk to one endpoint. Breeze Router matches each request to a healthy GPU with the memory to serve it, and keeps applications insulated from worker credentials and network topology.
- 01Connect
Enroll each machine with a one-time claim. Workers connect outward over HTTPS — nothing listens, nothing is port-forwarded.
- 02Route
One OpenAI-compatible endpoint with scoped per-app keys and model aliases. Requests land on the GPU that can actually serve them.
- 03Run
Models run in narrow, signed service profiles on the machine with the VRAM — your gaming PC included.
Breeze Console
The whole fleet, one pane of glass.
Live utilization, memory, temperature, and process telemetry for every enrolled machine — alongside queueing, placement, and an append-only activity trail. These are real Console views of a demo fleet. Flip through them.
First-class Windows workers
The biggest GPU you own probably runs Windows.
Most orchestration stacks stop at Linux. TensorBreeze treats a Windows machine with WSL2 as a first-class worker — enrolled, monitored, and serving models like any other node, whether it’s a workstation or the PC you game on.
- WSL2 aware
- A keepalive supervisor handles the realities of GPUs inside WSL2 so you don’t have to.
- Tray status
- A system-tray menu shows fleet state from the desktop. No terminal required.
- No dual boot
- Enroll once, keep Windows.
Designed for private infrastructure
A compromised Controller should not become a shell on every worker.
Workers connect outward and expose narrow inference contracts — never SSH, filesystems, or arbitrary commands. Every capability has its own credential, route, and schema, and everything dangerous ships switched off.
- Outbound-only workers with per-capability credentials
- Offline-signed releases with anti-rollback updates
- Content-free audit: tokens and latency, never prompts
- Per-application connector credentials and model aliases
No magic, on purpose
What TensorBreeze is not.
Clear boundaries are a feature. These are permanent product truths, not temporary limitations.
- Not remote CUDA
- TensorBreeze never pretends a remote GPU is a local device. Work goes to the machine; the machine does the work.
- Not a job runner
- No arbitrary commands, no remote shells. Workers run signed, allowlisted service profiles — nothing else.
- Not a cloud
- Your Controller belongs to your network. Nothing routes through ours.
Apps
A small catalog, on purpose.
Breeze separates client apps from inference providers, giving each integration only the credential and network access it needs. The catalog grows deliberately — reviewed and scoped, not scraped.
Open WebUI
Private chat through a scoped Breeze connector.
Early accessAnything OpenAI-compatible
Your own tools and agents point at the Router like any other endpoint.
By designEarly access