NVIDIA B200
Next-generation Blackwell GPU capacity planned for future rollout.
Serverless GPU
for your models
Deploy image and custom model endpoints on NVIDIA GPUs in seconds. Autoscaling, billing, monitoring, and SDKs are baked in — you just send requests.
Pay per second · H100 capacity · No long-term lock-in
- Cologix
- CenterSquare
- NVIDIA
- Supermicro
- Intel
- Dell
- HPE
- MIT
- Stanford University
- Caltech
- Bitdeer
- Quanta Computer
Get Started
One platform for every GPU workload
Start with a hosted app, call models through HTTP, or run controlled workloads on Kubernetes and VMs. Each path lands on the same RemoteGPU platform layer.
Storage, networking, access, metering, and runtime status stay shared as the workload moves from prototype to production.
Pricing built for scale
More GPU compute, lower cloud spend
RemoteGPU delivers competitive on-demand pricing for production workloads.
- RemoteGPUKubernetes H100
- RunPodH100 SXM on-demand, normalized
- CoreWeaveHGX H100 on-demand, normalized
- AWSEC2 P5 on-demand, normalized
- Google CloudA3 H100 on-demand, normalized
GPU cloud
Cloud GPU products for AI workloads
Start with available H100 plans today. Additional GPU models will get dedicated pages when pricing and launch paths are ready.
AvailablePlanned Planned Planned Planned
NVIDIA H100
H100 capacity for production inference, batch jobs, fine-tuning, and image workloads.
From $2.19/hrNVIDIA B300
Future Blackwell GPU capacity under evaluation for RemoteGPU.
NVIDIA GeForce RTX 5090
Consumer-class GPU capacity planned for cost-sensitive workloads.
NVIDIA RTX PRO 6000
Professional RTX GPU capacity planned for workstation-style workloads.
Success stories
See what AI teams build with RemoteGPU
From robotics labs to creative pipelines and market intelligence, teams use RemoteGPU to scale GPU workloads without waiting on local capacity.
Robotics training
Caltech Robotics Lab
For the robotics collaboration, RemoteGPU gives Caltech researchers elastic GPU capacity for policy training, simulation sweeps, and evaluation runs. The team can move between lab experiments and repeatable cloud jobs without waiting on local machines or rebuilding the same runtime for every research cycle.
Video generation
AI Video Studios
AI video studios use RemoteGPU to turn local ComfyUI and video generation pipelines into shared, persistent GPU workspaces. Artists can keep model assets, workflow graphs, and generated outputs together while engineers scale GPU capacity behind the scenes, making the same pipeline useful for prototypes, reviews, and production handoff.
Market intelligence
Quant Trading Firms
Quant trading firms use RemoteGPU's LLM API to analyze market news, filings, and real-time events before signals reach downstream models. Their research teams can also run Kubernetes training jobs for forecasting, ranking, and risk models while keeping inference and training infrastructure on the same GPU platform.
Search questions
Serverless GPU inference, hosted ComfyUI, and GPU workloads on Kubernetes
What can I run on RemoteGPU AI Cloud?
Start with hosted GPU applications, serverless inference endpoints, or GPU workloads on Kubernetes, then add storage and network primitives as the workload grows.
When should I use serverless GPU inference?
Use it when an application needs to call model work over HTTP without your team managing GPU servers, queues, scaling, or request billing.
When should I run GPU workloads on Kubernetes?
Use Kubernetes when you need direct control over deployments, jobs, services, ingress, storage, networking, and production operations.
Start building
Start building on RemoteGPU
Create an account, launch a hosted GPU workspace, or send your first inference request.