Popular Cloud GPU AI tools

21 category leaders in Cloud GPU, selected from the full directory.

Lambda

  • GPU workstations, servers, and on-demand cloud for ML.
  • Single vendor path from desk-side GPUs to data-center scale.
  • Popular with research labs and applied deep-learning teams.
  • Known for Lambda-branded hardware plus hosted clusters.

CoreWeave

  • Massive Nvidia GPU fleet for AI training and inference.
  • Dense networking tuned for multi-thousand-GPU jobs.
  • Deep ties to leading model labs and enterprise AI programs.
  • Dedicated AI cloud that competes head-on with hyperscalers.

RunPod

  • On-demand and serverless GPU hosts for builders.
  • Very large community on shared and dedicated clusters.
  • Containers, volumes, and templates for train and inference.
  • Common path from hobby fine-tunes to production workloads.

Modal

  • Serverless Python for GPUs, CPUs, and secure sandboxes.
  • Very fast cold starts for functions and batch jobs.
  • Code-first deploy from repos with strong developer experience.
  • Popular with ML and agent teams shipping iterative workloads.

Crusoe

  • GPU cloud built to reuse stranded and low-cost energy.
  • Large clusters for enterprise training and inference.
  • Reserved and on-demand capacity for long-running jobs.
  • Known for tying AI compute to sustainability narratives.

Vast

  • Large peer-to-peer GPU marketplace with global host inventory.
  • Very common choice for low-cost training, fine-tuning, and experiments.
  • Templates, containers, and on-demand instances across many GPU types.
  • Household name among ML builders comparing price and flexibility.

FluidStack

  • Enterprise bare-metal GPU clusters and Atlas OS for large AI jobs.
  • Lighthouse-style monitoring and optimization for serious multi-node scale.
  • High-profile partnerships with frontier labs and large AI programs.
  • Frequently cited in dedicated AI datacenter and HPC-cloud conversations.

Voltage Park

  • Neo-cloud with 24,000+ owned NVIDIA H100 and Blackwell GPUs.
  • On-demand HGX nodes from ~$1.99/hr with InfiniBand clusters.
  • Reference Platform NVIDIA Cloud Partner for enterprise AI factories.
  • Backed by $1B+ fleet; acquired TensorDock for elastic GPU access.

Nebius

  • AI-native neo-cloud for large GPU training jobs.
  • H100, H200, and Blackwell-class cluster capacity.
  • Built for frontier-scale training and inference.
  • Major independent alternative to hyperscaler GPU clouds.

Salad

  • Distributed GPU cloud with 60k+ daily active GPUs.
  • Very low-cost batch inference and container jobs.
  • Consumer and prosumer inventory at cloud scale.
  • Go-to budget GPU cloud for indie ML workloads.

Nscale

  • European neo-cloud for bare-metal AI GPU clusters.
  • Kubernetes, Slurm, inference, and fine-tune services.
  • Acquired Anyscale; large sovereign AI-factory buildout.
  • Top-tier alternative to US hyperscaler GPU clouds.

Hyperstack

  • On-demand NVIDIA GPUs for train and inference.
  • Self-serve instances with InfiniBand cluster options.
  • Common pick in European and global neocloud roundups.
  • Transparent hourly pricing for AI teams and labs.

Massed Compute

  • NVIDIA Preferred Partner cloud with owned GPU fleet.
  • On-demand instances plus single-tenant bare metal.
  • InfiniBand clusters for multi-GPU training jobs.
  • REST and MCP APIs for programmatic GPU rental.

TensorWave

  • AI cloud built around AMD Instinct accelerators.
  • High-memory GPUs for training and inference at scale.
  • Networking and storage paths tuned for AMD fleets.
  • Leading AMD-focused alternative to NVIDIA neoclouds.

Verda

  • European full-stack AI cloud (formerly DataCrunch).
  • GPU clusters, VMs, batch, and serverless inference.
  • EU data residency with competitive H100 and B200 rates.
  • Self-serve neocloud staple for European AI teams.

Hot Aisle

  • Automated inference cloud on AMD Instinct GPUs.
  • Sovereign production serving with high-bandwidth fabric.
  • Self-serve capacity without NVIDIA-only lock-in.
  • Developer-first AMD inference alternative in neocloud lists.

Amazon EC2 P5

  • AWS H100 instances for large-scale model training.
  • Elastic Fabric Adapter for multi-node GPU clusters.
  • Default hyperscaler path for enterprise AI workloads.
  • Pairs with SageMaker and broader AWS ML stack.

Google Cloud GPU

  • NVIDIA GPUs on Compute Engine and GKE for AI.
  • A3/A2 families for training and inference at scale.
  • Deep integration with Vertex AI and Google Cloud.
  • Hyperscaler GPU capacity alongside Google TPUs.

Azure GPU

  • Azure NC/ND/NV GPU VMs for training and inference.
  • NVIDIA and AMD accelerators including H100-class.
  • Enterprise AI and HPC across Azure regions.
  • Hyperscaler GPU default for Microsoft Cloud customers.

Oracle Cloud GPU

  • OCI bare-metal and VM GPU shapes for AI workloads.
  • NVIDIA GPUs with RDMA cluster networking options.
  • Enterprise AI training and inference on OCI.
  • Hyperscaler GPU path for Oracle Cloud customers.

Cerebrium

  • Serverless GPU infrastructure for real-time AI apps.
  • Auto-scaling model deploy without managing clusters.
  • Pay-per-use GPUs for inference and fine-tuning.
  • Popular serverless GPU cloud for ML engineers.