Run PyTorch inference on powerful remote GPUs from any device. A $6 VPS. A Raspberry Pi. A Chromebook. If it runs Python, it runs ML now.
# No GPU on your machine? No problem.
import remotorch
# Connect to a remote RTX 4090
remotorch.connect(
api_key="rk_...",
gpu_type="rtx4090" # Choose your GPU
)
# Use PyTorch as normal - tensors live on remote GPU
x = remotorch.tensor([1.0, 2.0, 3.0])
y = remotorch.tensor([4.0, 5.0, 6.0])
z = x + y # Computed on remote GPU
print(z.cpu()) # tensor([5., 7., 9.])Choose the right GPU for your workload. Pay only for what you use.
We're bringing more GPUs online. Check back soon!
Focus on your models, not infrastructure
Connect to a GPU in seconds. No provisioning, no waiting for instances to boot. Just connect and compute.
Use familiar PyTorch syntax. Your existing code works with minimal changes. All standard operations supported.
Each session runs in an isolated container. Your models and data are private and encrypted in transit.
Upload your models once, load them instantly. No need to transfer weights every session.
Track GPU hours, monitor sessions, and manage API keys from a beautiful dashboard.
Prepaid balance, billed per minute. No subscriptions, no idle charges. Your balance never expires.
Your code runs locally. The GPU computation happens remotely. No CUDA installation. No GPU drivers. No expensive hardware.
Deploy an ML-powered API on the cheapest DigitalOcean or Hetzner droplet. No GPU instance needed.
Run image classification, object detection, or LLM inference from a $35 Pi at the edge.
Develop and test ML models on your laptop without worrying about CUDA compatibility.
Add GPU inference to serverless functions. No cold starts loading massive models.
Run ML tests in GitHub Actions without expensive GPU runners. Pay only for actual compute.
Dust off your 2015 ThinkPad. If it runs Python, it can now run GPU inference.
Run Stable Diffusion from a $200 Chromebook with Linux enabled. Seriously.
Ship 50MB Docker images instead of 15GB GPU containers. No nvidia-docker needed.
Why pay $300+/month for a GPU instance that sits idle 90% of the time? With Remotorch, you only pay for actual GPU seconds used.
*Based on pay-as-you-go pricing with typical inference workloads. Your usage may vary.
Get running in under 5 minutes
Sign up for free and create an API key from your dashboard.
Connect and run your PyTorch code on remote GPUs instantly.
No subscriptions. No commitments. Only pay for what you use.
Top up your account with $10, $25, $50, or $100. Your balance never expires.
Run your workloads on any available GPU. Billed per minute of actual usage.
Optionally enable auto-refresh to keep your balance topped up automatically.
Add funds and pay only for what you use. No monthly fees.
For teams with high-volume needs
Add funds to your account balance, then use GPUs as needed. Your balance is deducted based on actual GPU time used, billed in 1-minute increments with a 1-minute minimum. Check your balance and transaction history anytime in your dashboard.
You need enough balance to cover at least 10 minutes of GPU time for the GPU type you're using. For example, an RTX 4090 at $0.50/hr requires about $0.09 minimum balance.
No, your balance never expires. Add funds whenever you need them and use at your own pace.
Auto-refresh automatically tops up your balance when it drops below a threshold you set. This ensures your workloads are never interrupted due to insufficient funds.
Join developers using Remotorch for their ML workloads.