Write Code. Click Run. Get results. No setup, no hardware, no hassle. Your code runs on powerful serverless GPUs at the cheapest price.
No CUDA installation. No driver nightmares. No pip dependency hell. Just paste your code and run. Everything is pre-configured.
No broker markup. We pass through compute costs directly. Same GPUs as other platforms, significantly lower price.
Only pay for the GPU time you actually use. Cancel anytime. A 30-second Stable Diffusion run costs less than one cent.
Most GPU work is iterative: run code, read output, tweak, repeat. With per-second billing, you only pay for actual compute time — not the minutes spent thinking between runs.
| Provider | T4 $/sec | L4 $/sec | A10G $/sec | A100-40 $/sec | A100-80 $/sec | H100 $/sec | Notes |
|---|---|---|---|---|---|---|---|
| SeqPU | $0.00016 | $0.00021 | $0.00031 | $0.00077 | $0.00103 | $0.00133 | Cheapest |
| RunPod | NA | NA | NA | NA | $0.00120 | $0.00154 | avg +16% |
| Replicate | $0.00023 | NA | NA | $0.00115 | $0.00140 | NA | avg +43% |
| Baseten | $0.00018 | $0.00024 | $0.00034 | NA | $0.00111 | $0.00181 | avg +13% |
Traditional Jupyter/Colab model: You pay for the entire session. A 1-hour session with 5 jobs × 30 seconds each = you pay for 60 minutes but only used 2.5 minutes of GPU.
SeqPU model: Container starts, runs your code, stops. You pay for 2.5 minutes. That's 24x more efficient.
Example: 5 training jobs × 30 sec over 1 hour
Three simple steps to run your first GPU-accelerated project
Write, paste or upload code that you would like ran on a GPU.
Choose from T4 to H100 based on your needs. We recommend the best option for your code.
Watch your code execute in real-time. Outputs are saved automatically for download.
Copy this code, paste it in SeqPU, select L4 GPU, and click Run
from diffusers import StableDiffusionPipeline
import torch
# Load Stable Diffusion (auto-downloads on first run)
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
# Generate an image from text
prompt = "a beautiful sunset over mountains, digital art"
image = pipe(prompt).images[0]
# Save the result
image.save("my_first_ai_image.png")
print("Image saved!")From AI art to protein folding, we have step-by-step examples for everything you can build with SeqPU.
Your code and data are protected. Here's how we keep things secure.
Each job runs in its own isolated container. Your code never touches other users' code or data.
Containers are destroyed after each run. No code or data persists on the GPU servers.
Your saved outputs and files are encrypted at rest. Only you can access your workspace.
We never use your code or outputs to train AI models. Your intellectual property stays yours.
Sign up in seconds. Get $0.85 free credits (~90 min on T4 GPU). No credit card required.