GPU cloud comparison · 2026
Jarvis Labs vs Salad
Jarvis Labs wins on 4 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
Salad
Distributed inference cloud — RTX 3090/4090 from $0.03/h
from $0.03/h
★★★★☆ 3.9 / 5 (423 reviews)
Try Salad →Head-to-Head Comparison
Jarvis Labs
Salad
Starting Price Lower hourly rate
from $0.39/h
from $0.03/h
Overall Rating User rating
4.3 / 5
3.9 / 5
GPU Types Variety
4 types
4 types
Max VRAM Largest available
80 GB
24 GB
Locations Regions covered
US, Asia
Global (distributed)
Wins out of 5
4
1
GPU Availability
Jarvis Labs
RTX 6000 AdaA100 40GBA100 80GBH100
VRAM: 48–80 GB · Locations: US, Asia
Salad
RTX 3090RTX 4090RTX 3080RTX 3070
VRAM: 8–24 GB · Locations: Global (distributed)
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
Salad
Pros
- Absurdly cheap — RTX 3090 from $0.03/h
- Massive horizontal scale (1000+ nodes)
- Auto-fleet management for inference
- No data-egress charges
Cons
- Distributed = no persistent storage
- Not suitable for training
- Latency varies by node geography
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
- Higher user satisfaction matters (4.3 vs 3.9)
- You want more GPU variety (4 vs 4 types)
Choose Salad if…
- You need GPU compute for Stateless inference
- You need GPU compute for Stable Diffusion bulk generation
- You need GPU compute for Embedding generation
- You need GPU compute for Cost-sensitive batch jobs
- Lower price is your top priority (from $0.03/h vs from $0.39/h)