Your own private AI GPU. No cloud. No monthly fees. A wireless eGPU bridge powered by Raspberry Pi.
A project by Leo Joachim Atienza
GhostGPU is an open-source wireless eGPU bridge system. It makes your laptop behave as if it has a local GPU — when in reality, the GPU lives on a separate machine connected over your home network.
A Pi acts as the network gateway — receiving workloads from your laptop over Wi-Fi and forwarding them to the GPU over wired PCIe/Ethernet.
Your data never leaves your network. No cloud uploads, no third-party servers, no subscriptions. Runs entirely on hardware you own.
Designed for local LLM inference, image generation, ML training, and batch compute — not gaming. Optimized for throughput over frame latency.
Millions of laptops and ultrabooks ship without a dedicated GPU. Cloud GPU services are expensive and require uploading private data to third-party servers. There's no affordable, private, open-source alternative — until now.
Cloud GPU services cost $0.50–$3.00/hr. For students, hobbyists, and indie devs, those costs add up fast — often exceeding the cost of owning hardware outright.
Solutions like SCUDA and rCUDA are pure software — they assume you already have server hardware. Nobody ships a dedicated Pi-based GPU bridge as a consumer product.
Uploading sensitive data, code, or documents to cloud GPU providers isn't always an option. GhostGPU keeps everything on your own network.
The signal path has exactly one wireless hop. Everything else is wired Gigabit Ethernet or direct PCIe. This eliminated the dual-wireless bottleneck from v1.0.
Everything you need to build GhostGPU. Estimated total: ~$235–320 CAD. Most components available on Amazon, eBay, Newegg, CanaKit, or PiShop.us.
| Component | Spec | Est. Cost (CAD) |
|---|---|---|
| AMD RX 580 8GB | gfx803 Polaris · 8GB GDDR5 | ~$40–55 |
| Raspberry Pi 5 | 4GB or 8GB RAM | ~$75–95 |
| Pineboards uPCIty Lite HAT | PCIe x4 open-ended slot | ~$45 |
| M.2 to OCuLink adapter | + ReDriver if needed | ~$20–30 |
| ATX PSU | 400W+ (used is fine) | ~$25–40 |
| MicroSD Card | 32GB+ Class 10 / A2 | ~$10–20 |
| Ethernet Cable | Cat5e or Cat6 | ~$5 |
| Active Cooler | Official Pi 5 or Pimoroni | ~$10 |
| ATX 24-pin Jumper | Run PSU standalone | ~$5 |
| Total estimated build cost | ~$235–320 | |
Ordered from simplest to most powerful. Start with Phase 1 to validate your hardware before investing time in Phase 2 or 3.
Run an AI model on the RX 580 over the network. Your laptop sends prompts via HTTP and receives outputs — no local GPU work. Uses ROCm with HIP backend for AMD GPU acceleration.
Transparent GPU offloading using PyTorch + a gRPC/HTTP server on the Pi. Your laptop code stays the same — you just swap the forward pass to hit the Pi. Full tensor serialization over the network.
If ROCm proves unstable, Phase 3 uses Vulkan compute — which the RX 580 supports fully (Vulkan 1.2) — as a hardware-agnostic alternative. Uses Kompute + FastAPI. Works with any Vulkan-capable GPU.
Critical configuration details and design decisions that will save you hours of debugging.
GhostGPU fills a specific gap: a dedicated, open-source, Pi-based hardware appliance for wireless GPU compute. Nobody else has shipped this as a consumer product.
Assumes you already have server hardware with a GPU. GhostGPU adds the dedicated Pi-based PCIe-to-network hardware gateway that SCUDA doesn't provide.
Locked to NVIDIA's proprietary ecosystem. GhostGPU targets AMD GPUs with fully open-source drivers that work on ARM (Pi).
Enterprise-grade GPU virtualization. Requires VMware licensing. GhostGPU is open-source and costs under $300 in hardware.
Proved PCIe on Pi 5 is viable. GhostGPU builds on this foundation to create a complete networked GPU bridge system with software stack.
Benchmarks from Phase 1 (llama.cpp) on the RX 580 8GB with wired Pi architecture.
LLM inference (Mistral, LLaMA, etc.) · Image generation (Stable Diffusion) · ML training on small–medium datasets · Batch compute and data processing
Real-time gaming (latency too high) · Live video encoding · Latency-critical applications under 1ms · Large-scale distributed training
GhostGPU is under active development. Here's where we're headed.
From zero to running LLM inference on your own GPU — in 7 steps.
Get everything from the bill of materials above. Used RX 580s are plentiful and cheap.
Write Raspberry Pi OS Lite (64-bit) to your microSD card using the Pi Imager.
Connect Pi to your router via Ethernet. Attach RX 580 via uPCIty Lite HAT + OCuLink adapter.
Install the AMD GPU compute stack. Verify with rocminfo | grep gfx803.
Clone and compile with -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx803.
Grab a GGUF model (e.g., Mistral 7B Q4) from Hugging Face and place it in the models directory.
Run the server, then point your laptop browser to http://<pi-ip>:8080 — done.
GhostGPU is open-source and welcomes contributions. Here are the areas where help is most needed:
Phase 3 Vulkan compute and full GPU API interception — the hardest unsolved piece of the project.
Testing with Intel Arc, newer AMD GPUs, multi-GPU setups, and different Pi configurations.
Improving setup documentation, writing tutorials, and creating video walkthroughs for the community.