LUPINE: GPU-over-IP

LUPINE is a GPU over IP bridge allowing GPUs on remote machines to be attached to CPU-only machines.

Hosted Demo

Connect to a hosted demo server with a T4 attached for free. This might take a while if there's no GPU currently provisioned, but subsequent requests should be faster.

$ docker run --rm \
  -e LUPINE_SERVER=demo.lupinemachines.com:14833 \
  ghcr.io/lupinemachines/lupine-client:cuda-13.3.1-ubuntu24.04 \
  nvidia-smi -L
GPU 0: Tesla T4 (via lupine demo.lupinemachines.com) (UUID: GPU-b80ae1b9-863f-8f91-7c63-d351fabff035)

Mac Demo

LUPINE lets you spin up a container with a virtual GPU, like connecting a Mac to a Linux GPU server.

% uname -mors 
Darwin 25.5.0 arm64
% uv run https://raw.githubusercontent.com/lupinemachines/lupine/main/python/examples/tensor.py
LUPINE server host: 100.106.167.98  <-- the ip of a machine with the LUPINE server running
LUPINE server port [14833]: 
cuda available: True
device: lupine:0
count: 1
gpu: NVIDIA GeForce RTX 4090
result: [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0]

Quick Start

Use the published GHCR images. The examples below pin CUDA 13.3.1 on Ubuntu 24.04; other published tags use the same cuda-<cuda-version>-ubuntu<ubuntu-version> format.

Run the server on the GPU machine:

docker run --rm --gpus all -p 14833:14833 \
  ghcr.io/lupinemachines/lupine-server:cuda-13.3.1-ubuntu24.04

Run the client pointing at that server:

docker run --rm -it \
  -e LUPINE_SERVER=<server>:14833 \
  ghcr.io/lupinemachines/lupine-client:cuda-13.3.1-ubuntu24.04 \
  nvidia-smi

Example output from a real run against a remote RTX 4090:

Mon May 18 15:40:46 2026
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.288.01             Driver Version: 590.48.01    CUDA Version: 13.1     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 4090        On  | 00000000:01:00.0  On |                  Off |
| 30%   52C    P8              22W / 450W |      8MiB / 24564MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

Inside the client container, LD_LIBRARY_PATH=/opt/lupine/lib is already set, so CUDA driver users pick up the LUPINE libcuda.so.1 shim and NVML users such as nvidia-smi pick up the LUPINE libnvidia-ml.so.1 shim automatically.

Graceful Server Checkpoints

On Linux, SIGTERM stops the server from accepting connections, asks every connection child to finish its in-flight CUDA calls, and waits for those children to exit. This graceful drain happens in the open-source server with no extra runtime dependency.

Each connection child looks for liblupinecr.so.0, then liblupinecr.so, and uses the versioned provider ABI in checkpoint_provider.h. A missing or incompatible provider is a no-op; the server still drains and exits normally. The provider is loaded before the child's first CUDA call so it can observe RM/UVM activity needed to discover allocations.

Set LUPINE_SESSION in the client to attach a stable connection identifier. The optional provider receives that identifier to restore the connection before its first CUDA RPC and checkpoint it after shutdown has drained. For an unkeyed connection, restore is skipped and checkpoint receives a null identifier. Providers own storage configuration, file layout, and any fallback policy for unkeyed connections; Lupine does not select a checkpoint directory.

LUPINE_CHECKPOINT_LIBRARY can override the provider library path for a private deployment.

Connection Stability

Each client/server connection is a single long-lived TCP stream. Long-running workloads sit idle for long stretches (between training steps, during host-side data loading, inside long kernels), and stateful middleboxes — cloud load balancers, NAT gateways, conntrack tables, firewalls — silently reap idle flows far sooner than the kernel's default 2-hour keepalive. The next RPC then fails fatally. Lupine keeps these connections alive and resilient without retrying RPCs (which would break CUDA semantics):

• TCP keepalive is enabled on every connection (client and server) with a 60s idle interval, 15s between probes, and 3 unanswered probes before giving up. Probes are sent only while idle, so active transfers pay no latency cost, and a dead peer is detected in ~105s instead of hanging on the TCP retransmit timer.
• Connect retry rides out a server that is not reachable yet (e.g. still provisioning): a connection is attempted a few times with exponential backoff, and each attempt is bounded by a deadline so a packet-filtered port is detected quickly rather than blocking for the full SYN-retransmit window.

Socket buffer sizes are left to the OS, which auto-tunes on modern kernels.

Trace Logging

Set LUPINE_TRACE on the client, server, or both to enable trace logging. LUPINE_TRACE=0 or an unset value disables tracing. LUPINE_TRACE=1 writes trace output to stdout, LUPINE_TRACE=2 writes it to stderr, and any other non-empty value is treated as a file path opened in append mode.

# trace to stdout
LUPINE_TRACE=1 ./your_cuda_program

# trace to stderr
LUPINE_TRACE=2 ./server

# trace to a file
LUPINE_TRACE=/tmp/lupine.trace ./your_cuda_program

The same LUPINE_TRACE variable controls both client and server tracing; LUPINE_SERVER_TRACE is no longer used.

Device printf Forwarding

LUPINE inspects uploaded PTX and cubin symbol data for vprintf, the CUDA device printf implementation. Until an image that may use device stdout is loaded, synchronization avoids stdout redirection and its process-global lock, allowing independent RPC lanes to synchronize concurrently. Fully opaque compressed fatbins are treated conservatively as potentially using device stdout.

After a device-output-capable image is loaded, context, stream, and event synchronization captures server fd 1 and forwards the bounded CUDA printf buffer to the client's stdout. Capture remains process-global so output from concurrent synchronization lanes is not misattributed.

Multi-GPU Across Multiple Servers

The client accepts a comma-separated LUPINE_SERVER list. Devices are exposed as one local ordinal list in server order: all GPUs from the first server, then all GPUs from the next server, and so on.

Run a server on each GPU machine:

# on gpu-host-a
docker run --rm --gpus all -p 14833:14833 \
  ghcr.io/lupinemachines/lupine-server:cuda-13.3.1-ubuntu24.04

# on gpu-host-b
docker run --rm --gpus all -p 14833:14833 \
  ghcr.io/lupinemachines/lupine-server:cuda-13.3.1-ubuntu24.04

Point the client at both servers:

docker run --rm --network host \
  -e LUPINE_SERVER=gpu-host-a:14833,gpu-host-b:14833 \
  ghcr.io/lupinemachines/lupine-client:cuda-13.3.1-ubuntu24.04 \
  nvidia-smi -L

Expected output lists both remote GPUs:

GPU 0: NVIDIA GeForce RTX 4090 (UUID: GPU-...)
GPU 1: NVIDIA GeForce RTX 4090 (UUID: GPU-...)

CUDA driver applications use the same LUPINE_SERVER value:

docker run --rm --network host \
  -e LUPINE_SERVER=gpu-host-a:14833,gpu-host-b:14833 \
  ghcr.io/lupinemachines/lupine-client:cuda-13.3.1-ubuntu24.04 \
  ./your_cuda_program

Cross-server device-to-device and peer (cuMemcpyDtoD / cuMemcpyPeer) copies are supported: when the source and destination live on different servers, the client transparently stages the data through itself (device->host on one server, then host->device on the other). Direct server-to-server transfers that avoid that client hop, cross-server peer-access enablement, and cuMemcpy3DPeer are not implemented yet. Same-server operations route by handle ownership.

Prefix an endpoint with https:// when the Lupine server is behind a TLS-terminating proxy. Both CUDA applications and NVML tools such as nvidia-smi use the scheme and verify the proxy certificate against the system trust store. HTTPS defaults to port 443; plain and http:// endpoints default to port 14833.

For a specific CUDA version:

docker pull ghcr.io/lupinemachines/lupine-client:cuda-12.4.1-ubuntu22.04
docker pull ghcr.io/lupinemachines/lupine-server:cuda-12.4.1-ubuntu22.04

Client images are also published with a -slim tag, for example ghcr.io/lupinemachines/lupine-client:cuda-13.3.1-ubuntu24.04-slim. The default client tag keeps the CUDA runtime libraries for applications that link against them; the slim tag includes only the LUPINE shims, their runtime dependencies, and nvidia-smi.

Slow Start for the Skeptics

This path derives a small PyTorch client image from the published LUPINE client image and runs the microgpt_train test against a remote GPU. It is intentionally explicit so it is easy to see which side is the CPU-only client and which side owns the GPU.

Create a PyTorch client Dockerfile in the repo root:

# Dockerfile.pytorch-lupine
FROM ghcr.io/lupinemachines/lupine-client:cuda-13.3.1-ubuntu24.04

ARG DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 \
    python3-pip \
    && rm -rf /var/lib/apt/lists/*

RUN pip3 install --break-system-packages \
    --index-url https://download.pytorch.org/whl/cu132 \
    torch

COPY test/pytorch_lupine_tests.py /opt/lupine/test/pytorch_lupine_tests.py

ENV LD_LIBRARY_PATH=/opt/lupine/lib:${LD_LIBRARY_PATH}

CMD ["python3", "/opt/lupine/test/pytorch_lupine_tests.py", "microgpt_train"]

Build it:

docker build -f Dockerfile.pytorch-lupine -t lupine-pytorch:cuda-13.3 .

Run the server on the GPU machine:

docker run --rm --gpus all -p 14833:14833 \
  ghcr.io/lupinemachines/lupine-server:cuda-13.3.1-ubuntu24.04

Run the PyTorch client from the CPU-only machine:

docker run --rm \
  -e LUPINE_SERVER=<server>:14833 \
  lupine-pytorch:cuda-13.3

Expected success looks like:

microgpt first_loss=... last_loss=...
microgpt_train: PASS

Local development

Building the binaries requires running codegen first. The repository provides a containerized runner so local development and CI use the same CUDA headers, Python, parser, and formatter versions. Docker is the only host dependency.

Run codegen

./codegen/run.sh

Ensure there are no errors in the output of the codegen.

Run cmake

cmake -S . -B build
cmake --build build

CMake builds the CUDA driver shim at build/libcuda.so.1, the NVML shim at build/libnvidia-ml.so.1, and the server at build/lupine_driver_server.

The Lupine server must be running before initiating client commands.

./local.sh server

If successful, the server will start:

Server listening on port 14833...

Running the client

For local development, preload the built libcuda.so.1 before executing CUDA commands. The published client image sets LD_LIBRARY_PATH for you instead.

Once the server above is running:

# update to your desired IP/port
export LUPINE_SERVER=<server>:14833

LD_PRELOAD=./build/libcuda.so.1 python3 -c "import torch; print(torch.cuda.is_available())"

# or

LD_PRELOAD=./build/libcuda.so.1 nvidia-smi

You can also use the local shell script to run your commands.

./local.sh run

Questions

1. What does LUPINE stand for? Nothing, it just looks cool in all caps.
2. Does this support authentication? TLS? Indirectly, yes. It's a plain HTTP/2 server, so you can front it with whatever TLS/auth server you want.
3. Was this repo AI-generated? A chunk of it, yes. I mean, would you want to hand write hundreds of tedious API stubs? No? Me neither.
4. Doesn't this incur a lot of latency? Surprisingly, no! You will see device transfers get slower because this is basically bottlenecking a PCIe link over the network, but there is very little overhead besides that. For things like model training and inference, once the model is on the GPU very little data transfer happens to the host. As a result, it might be faster than you expect.
5. Can I do remote video encoding/decoding? This is probably one use case we wouldn't recommend because that's a lot heavier on the PCIe link. It works in theory though, so if you do have access to a 1 Tbps link it might work for you.

Prior Art

This project is inspired by some existing proprietary solutions:

• https://www.thundercompute.com/
• https://www.juicelabs.co/
• https://en.wikipedia.org/wiki/RCUDA