GPU cloud comparison · 2026
Hyperstack vs Jarvis Labs
Hyperstack wins on 5 of 5 key metrics — but the right choice depends on your workload.
Overall Winner
Hyperstack
Global GPU cloud specialist — H100, A100 80GB and L40 from $0.11/h
from $0.11/h
★★★★☆ 4.3 / 5 (198 reviews)
Try Hyperstack →VS
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 →Head-to-Head Comparison
Hyperstack
Jarvis Labs
Starting Price Lower hourly rate
from $0.11/h
from $0.39/h
Overall Rating User rating
4.3 / 5
4.3 / 5
GPU Types Variety
5 types
4 types
Max VRAM Largest available
80 GB
80 GB
Locations Regions covered
UK, EU
US, Asia
Wins out of 5
5
0
GPU Availability
Hyperstack
RTX A6000A100 80GBH100L40L40S
VRAM: 48–80 GB · Locations: UK, EU
Jarvis Labs
RTX 6000 AdaA100 40GBA100 80GBH100
VRAM: 48–80 GB · Locations: US, Asia
Pros & Cons
Hyperstack
Pros
- Outstanding entry pricing for A6000
- Full networking stack (VPC, firewall, NAT)
- UK / EU regions for European latency
- Reservation discount up to 75%
Cons
- No B200 / H200 yet (April 2026)
- Smaller marketing footprint than RunPod
- Limited template marketplace
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
Which Should You Choose?
Choose Hyperstack if…
- You need GPU compute for Budget training jobs
- You need GPU compute for Stable Diffusion at scale
- You need GPU compute for VPC-isolated workloads
- You need GPU compute for EU-friendly compute
- Lower price is your top priority (from $0.11/h vs from $0.39/h)
- Higher user satisfaction matters (4.3 vs 4.3)
- You want more GPU variety (5 vs 4 types)
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