Focus on the Thesis.
We'll Watch the Logs.
The first GPU cloud that bills for results, not uptime. No more 3 AM alarms to check training status.
No more paying for "ghost hours" on crashed runs.
Stop babysitting drivers and start publishing.
The Hidden Tax on AI Research
Every researcher knows these nightmares. We built QED to end them.
The 3 AM Crash
Your training crashes at 3 AM due to CUDA_OUT_OF_MEMORY. You wake up to a $400 bill for an idle machine running nothing.
Dependency Hell
CUDA 12.1 conflicts with PyTorch 2.0. cuDNN mismatch. Docker image 40GB. Hours lost before training even starts.
The QED Solution
When crashes happen, our Research Engineers fix it while you sleep. You only pay for actual compute—never for intervention time or idle GPUs.
Average researcher wastes $2,400/month on crashed runs and idle machines.
The True Cost of Training Issues
See how intervention time and idle GPUs add up
Traditional Cloud
You pay for setup and idle time
QED Compute
Only pay for actual training compute
Your monthly savings with QED
Free provisioning + 10.0h of engineer fixes
$4.00
20% saved
Simple, Transparent Pricing
Lower base rates than AWS, GCP, and CoreWeave—plus you only pay for actual execution time, not setup or downtime, but with concierge support.
Base Rate
Lower
than major clouds
Setup & Provisioning
$0
always free
Intervention & Fixes
$0
we absorb the cost
NVIDIA RTX 4090
24GB VRAM
Great for fine-tuning and smaller models
$0.74typical rate elsewhere
- Up to 8 GPUs per job
- Free provisioning & setup
- Free intervention & fixes
- Concierge support included
Your Effective Cost Is Even Lower
With traditional clouds, you pay for idle time during crashes, setup overhead, and time spent debugging environment issues. At QED, you only pay when your training is actually running. For a typical research workload with 20-30% intervention time, your effective savings can exceed 50% compared to other providers.
All prices are per GPU per hour of execution time only. You're never charged for provisioning, intervention, or idle time.
How QED Works
Five states. One simple rule: you only pay during successful execution.
Submit Your Brief
$0Provide your GitHub repo URL, branch, dataset source, and success criteria. Add any special instructions for our Research Engineers.
We Provision
$0Our team sets up your environment—CUDA, PyTorch, dependencies, data loading. You don't pay for setup time.
Training Executes
Billing ActiveYour model trains. This is the only state where billing is active. Real-time logs and metrics available in your dashboard.
We Intervene (If Needed)
$0If something crashes or needs adjustment, we pause billing and fix it. You're notified, but you don't pay for our intervention time.
Success & Artifacts
$0Training complete. Download your model weights, logs, and auto-generated BibTeX. Ready for your Methods section.
The State Machine
Only the Executing state bills compute time
Built for Serious Research
Every feature designed around one principle: your time and grant money should go toward publishing papers, not fighting infrastructure.
Zero-Waste Billing
Our state machine tracks every second. If your run crashes or needs intervention, the billing clock stops immediately. You only pay for successful execution.
Pay for results, not uptime
Environment Concierge
Our Research Engineers handle CUDA conflicts, dependency hell, and environment setup. Submit your repo URL and success criteria—we handle the rest.
No more DevOps headaches
Grant-Ready Artifacts
Download your .safetensors weights, training logs, and auto-generated BibTeX citations. Everything you need for reproducibility and publication.
Ready for peer review
The Night Watch
If something breaks at 3 AM, we catch it. Our team monitors runs and intervenes on failures—you wake up to a success notification, not a bill for ghost hours.
Sleep while we watch
Transparent Time Tracking
See exactly when billing is active and when it's paused. Every state transition is logged. Full visibility into what you're paying for.
No hidden costs
Research Brief System
Submit structured briefs with your GitHub URL, branch, dataset source, and success criteria. No chat interfaces—just professional, reproducible runs.
Structured, not chatty