Find the right GPU cloud for what you actually want to run.
Compare GPU providers for AI video, image generation, LLMs and training — without decoding enterprise cloud jargon.
AI image generation
LLM inference
Model fine-tuning
Bursty production API
Experimenting and testing
Start with what you want to run
GPU hosting providers
Filter by what you want to run, rather than by infrastructure terminology.
CUDO Compute
Enterprise GPU infrastructure and cluster operations for AI workloads.
Hyperstack
GPU virtual machines with API access for AI training, inference and rendering.
Jarvislabs
GPU virtual machines, development templates and serverless deployments for AI workloads.
TensorDock
GPU cloud virtual machines for deep learning, AI and rendering, with deployment APIs.
Thunder Compute
GPU instances and sandboxes for AI development, evaluations and post-training.
fal
Image, video and audio model APIs, serverless GPUs and dedicated compute for model development.
Lambda
On-demand GPU instances and clusters for model training and inference, with API and CLI access.
Modal
Serverless GPU compute for model inference, fine-tuning, training and evaluation.
Paperspace / DigitalOcean
DigitalOcean Paperspace provides GPU virtual machines with published machine configurations.
Replicate
Hosted APIs for image, video and language models, with fine-tuning and custom deployments.
RunPod
GPU Pods for container workloads alongside serverless inference and deployment APIs.
Together AI
Hosted model inference, fine-tuning and GPU compute for AI development.
Vast.ai
Marketplace GPU containers for training, inference and development, plus serverless deployments.
Verda
Verda provides GPU instances and serverless containers for training and inference.
CloudRift
GPU rentals using virtual machines, containers and bare metal, plus hosted inference endpoints.
CoreWeave
GPU infrastructure with managed Kubernetes for training and inference.
Fluidstack
Large-scale AI compute and data-center infrastructure for enterprise workloads.
Gcore
GPU virtual machines and bare metal for AI training, inference and accelerated computing.
io.net
Distributed GPU infrastructure accessed through IO Cloud.
Latitude.sh
Dedicated bare-metal GPU infrastructure for machine-learning workloads.
Nebius
AI cloud infrastructure for model training and inference, including hosted model services.
OVHcloud
Public-cloud GPU instances and AI services for training and inference.
Radiant / Ori
Radiant's GPU infrastructure includes virtual machines and bare-metal compute.
Scaleway
GPU instances for AI development, model training and inference.
Voltage Park
Bare-metal GPU clusters for large-scale AI training.
Vultr
GPU virtual machines for AI, machine learning and accelerated computing.
Amazon Web Services
Amazon EC2 offers GPU virtual machines for machine learning and accelerated computing.
Google Cloud
Compute Engine provides GPU-backed virtual machines with configurable machine types.
Microsoft Azure
Azure provides GPU virtual machines for AI and high-performance computing.
Oracle Cloud
GPU virtual machines and bare-metal instances for AI training and accelerated computing.
Pod or serverless?
For most people, this is the first decision that matters.
GPU Pod / VM
A GPU machine stays allocated to you while it is running.
- Best for steady or continuous work
- Predictable environment
- You pay while the machine is running
- Concurrent jobs usually need your own queue/workers
Serverless GPU
Workers spin up for jobs and scale with demand.
- Best for bursts and unpredictable traffic
- Multiple requests can scale across workers
- You mainly pay for execution time
- Cold starts can matter
What does one generation actually cost?
Hourly rates are hard to compare. Convert them into an approximate cost per image, video or inference job.
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