Run inference on your own GPUs

Install a single binary or Docker container. Your prompts and completions stay on your hardware — routed through the same API, the same SDKs, the same console.

Why self-host

Installation

Install with the native package for your OS or run as a Docker container. On a Linux server the quickest path is curl -sSL https://get.pendra.ai/worker | sh, which auto-detects your GPU; you can instead add the Pendra apt or yum repository so apt upgrade / dnf upgrade keeps the worker current. On macOS and Windows, open the Pendra console → Workers → Add worker for an OS-aware download with checksums. Full per-OS instructions live in Workers → Install.

# Linux server — one command, auto-detects your GPU
curl -sSL https://get.pendra.ai/worker | sh

# …or install manually:
# macOS (Apple Silicon) — open the .dmg, drag Pendra into /Applications
open Pendra-<v>-arm64.dmg

# Windows — run the signed installer, no UAC needed
PendraSetup-<v>.exe

# Linux — pick CPU / CUDA / Vulkan to match your GPU
sudo apt install ./pendra_<v>_linux_amd64.deb
sudo pendra setup   # writes /var/lib/pendra/config.yaml and restarts pendra.service

# docker-compose.yml
services:
  pendra-worker:
    image: ghcr.io/pendra-cloud/pendra-worker:latest
    restart: unless-stopped
    environment:
      - GPU_WORKER_PRIVATE_KEY=your_key_here
    extra_hosts:
      - "host.docker.internal:host-gateway"

# docker-compose.yml
services:
  pendra-worker:
    image: ghcr.io/pendra-cloud/pendra-worker:cuda
    restart: unless-stopped
    environment:
      - GPU_WORKER_PRIVATE_KEY=your_key_here
    extra_hosts:
      - "host.docker.internal:host-gateway"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

# docker-compose.yml
services:
  pendra-worker:
    image: ghcr.io/pendra-cloud/pendra-worker:vulkan
    restart: unless-stopped
    environment:
      - GPU_WORKER_PRIVATE_KEY=your_key_here
    extra_hosts:
      - "host.docker.internal:host-gateway"
    devices:
      - /dev/dri:/dev/dri

CLI reference

The pendra CLI manages your worker. Config is stored in ~/.pendra/config.yaml. Full env-var reference at Workers → Configuration.

Models

A worker serves models from Pendra's curated catalogue — vetted open-source models (Llama, Qwen, Mistral, gpt-oss, Phi, embeddings, and more). Install one with a single click in the console (Models → Install) or with pendra models install <model>; Pendra downloads a verified GGUF straight onto the worker, no SSH or shell required. If the worker drops offline mid-download, the install picks up where it left off when it reconnects — it doesn't restart from zero, and the console shows the download as interrupted until it resumes. Out of the box a worker handles chat (with reasoning, tool calling, structured outputs, and vision), embeddings, image generation, and audio transcription — all in-process, with nothing else to install. See choosing a model size and choosing a quantization to pick the right variant for your hardware.

Requirements

Hybrid deployments

Route traffic based on sensitivity, cost, or performance. Your application code stays the same regardless of where inference runs.

Want us to handle it instead?