Remotorch is a platform that makes AI accessible. We provide two complementary ways to work with ML models: REST APIs for app developers and remote GPU access for Python developers.
For app developers
Call pre-configured AI models via simple REST APIs. No ML knowledge required. Upload an image, get back an upscaled version. Send text, get embeddings. Works from any language that can make HTTP requests.
For Python/PyTorch developers
Run PyTorch code on remote GPUs from any device. Your code executes locally, but tensor operations happen on our GPUs. Perfect for IoT devices, development machines without GPUs, and edge deployments.
Understanding where Remotorch fits in the AI ecosystem
Bottom line: HuggingFace is where you find models. Remotorch is where you run them without needing ML infrastructure expertise. We actually import many models from HuggingFace and make them accessible via simple APIs.
Bottom line: Cloud GPU providers give you raw compute. Remotorch gives you ready-to-use AI capabilities. Use cloud GPUs for training large models or running 24/7 production workloads. Use Remotorch for everything else.
Bottom line: If you have a local GPU and need low latency, use it. Remotorch is for everyone else: developers without GPUs, IoT deployments, occasional GPU users, and anyone who wants to learn without investing in hardware first.
Who uses Remotorch and why
You're building an app and want to add AI features: image processing, text analysis, object detection. You don't want to become an ML engineer to do it.
Upload a sample file, see the output, understand what you're working with before writing code.
The playground generates the exact API call. Copy it, paste it into your app, it works.
Node.js, Python, Ruby, Go, Rust - if it can make HTTP requests, it works.
// Node.js / Express endpoint
app.post('/upscale', async (req, res) => {
const response = await fetch(
'https://api.remotorch.io/v1/models/swinir/infer',
{
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.REMOTORCH_KEY}`
},
body: req.file
}
);
const upscaledImage = await response.blob();
res.send(upscaledImage);
});# Running on a Raspberry Pi 4
import remotorch
from picamera2 import Picamera2
# Connect to remote GPU (RTX 4090)
remotorch.connect(
api_key="rk_...",
gpu_type="rtx4090"
)
# Load your detection model (stored on Remotorch)
model = remotorch.load_model("my-yolo-model")
# Capture from Pi camera
camera = Picamera2()
frame = camera.capture_array()
# Run inference on remote GPU
detections = model(remotorch.tensor(frame))
print(detections.cpu()) # Results back on PiYou're building something on a Raspberry Pi, Jetson Nano, or other embedded device. You need to run ML models but don't have local GPU resources.
Export your model once, upload it, then load and run it from any device.
Your existing PyTorch code works with minimal changes. Tensors live on remote GPU.
Raspberry Pi, Chromebook, WSL, old laptop - if it runs Python, it can access GPUs.
You want to learn how to integrate AI into applications but don't have the budget for GPU hardware and don't want to spend weeks setting up environments.
The playground lets you experiment immediately without any setup.
Watch the actual HTTP requests being made. Understand the protocol.
Start free, pay cents per experiment. No $1000 GPU investment to start learning.
We want you to succeed, which means being honest about what we're good at
We'd rather tell you upfront than have you discover limitations after investing time. For high-volume production, consider dedicated GPU instances once you've validated your approach with us.
Try the playground, explore our models, and see if Remotorch is right for you.