GPU cloud comparison · 2026
Jarvis Labs vs Paperspace
Jarvis Labs wins on 5 of 5 key metrics — but the right choice depends on your workload.
Overall Winner
Jarvis Labs
On-demand H100 / A100 / RTX 6000 Ada from $0.39/h
from $0.39/h
★★★★☆ 4.3 / 5 (312 reviews)
Try Jarvis Labs →VS
Paperspace
Gradient notebooks + GPU VMs — great for ML teams
from $0.45/h
★★★★☆ 4.3 / 5 (1,456 reviews)
Try Paperspace →Head-to-Head Comparison
Jarvis Labs
Paperspace
Starting Price Lower hourly rate
from $0.39/h
from $0.45/h
Overall Rating User rating
4.3 / 5
4.3 / 5
GPU Types Variety
4 types
4 types
Max VRAM Largest available
80 GB
80 GB
Locations Regions covered
US, Asia
US, EU
Wins out of 5
5
0
GPU Availability
Jarvis Labs
RTX 6000 AdaA100 40GBA100 80GBH100
VRAM: 48–80 GB · Locations: US, Asia
Paperspace
A100A6000RTX 4000V100
VRAM: 8–80 GB · Locations: US, EU
Pros & Cons
Jarvis Labs
Pros
- Excellent pricing for H100
- RTX 6000 Ada — 48GB at moderate cost
- Polished UI for non-DevOps users
- Quick spinup, low friction
Cons
- Smaller GPU variety than RunPod
- No serverless / autoscaling
- Limited European presence
Paperspace
Pros
- Best notebook experience of any cloud GPU
- Team collaboration features built-in
- Free tier with limited GPU hours
- Good documentation and tutorials
Cons
- Pricier than RunPod for raw compute
- Limited GPU types vs competitors
- Gradient platform has occasional issues
Which Should You Choose?
Choose Jarvis Labs if…
- You need GPU compute for Researchers and indie developers
- You need GPU compute for Llama fine-tuning
- You need GPU compute for Stable Diffusion training
- You need GPU compute for Jupyter notebook users
- Lower price is your top priority (from $0.39/h vs from $0.45/h)
- Higher user satisfaction matters (4.3 vs 4.3)
- You want more GPU variety (4 vs 4 types)
Choose Paperspace if…
- You need GPU compute for Notebooks
- You need GPU compute for ML teams
- You need GPU compute for Prototyping
- You need GPU compute for Education