Popular - Cloud GPU(Core AI)
Updated: 2 Days AgoCloud GPU
- AI-native neo-cloud built specifically for GPU training and inference.
- NVIDIA partner with H100, H200, and Blackwell across owned DCs.
- Gigawatt-scale expansion backed by deep NVIDIA stack partnership.
- European roots with rapidly growing U.S. AI factory footprint.
- EU-sovereign GPU cloud with GDPR residency across European DCs.
- VMs, serverless runs, and clusters scaling to 8,000 GPUs.
- Per-second billing with compiler-level workload profiling.
- €10M+ pre-seed; Berlin-founded alternative to U.S. neo-clouds.
- 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.
- 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.
- Multi-cloud control plane for GPU training, fine-tuning, and inference.
- Unifies hyperscalers and neo-clouds with hosted and BYOC compute paths.
- Strong positioning on utilization, orchestration, and cost-aware clusters.
- Popular with enterprises standardizing ML infrastructure across clouds.
- Established global cloud with GPU cloud and bare metal options.
- Broad region footprint and straightforward provisioning for AI workloads.
- Frequent alternative when teams want non-hyperscaler GPU capacity.
- Widely recognized developer cloud brand beyond niche ML vendors.
- 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.
- 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.
- 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.
- 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.
- 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.
- Multi-cloud GPU marketplace with algorithmic pricing.
- Aggregates capacity for large training and inference jobs.
- Early traction with frontier labs and research groups.
- High-profile backing in AI infrastructure circles.
- 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.
- GPU cloud for training and inference with clear pricing.
- Strong European footprint and HPC-style cluster options.
- Targets researchers, startups, and cost-sensitive ML teams.
- Positioning around performance per dollar versus hyperscalers.
- GPU notebooks, batch jobs, and clusters for ML teams.
- Managed Kubernetes and simple paths from prototype to scale.
- Now part of DigitalOcean’s cloud portfolio.
- Popular when teams want less hyperscaler overhead.