AI TrustMark

GPU / compute platforms companies, products & suppliers

GPU and accelerator capacity for training or inference, not a model product. Jobs: gpu vms; reserved clusters; multi-node training fabric.

What are GPU / compute platforms?

GPU and accelerator capacity for training or inference, not a model product. Jobs: gpu vms; reserved clusters; multi-node training fabric.

What problems does it solve?

Teams cannot get compliant accelerators at the region and tenancy they need.

Typical business use cases

  • GPU VMs
  • Reserved clusters
  • Multi-node training fabric

Important capabilities

  • Accelerator types
  • Regions
  • Orchestration

What buyers should evaluate

  • Region
  • Tenancy
  • Data gravity

Risks and governance considerations

Training data sitting on someone else's GPU without isolation.

Procurement checklist

  • Isolation
  • Region
  • Exit of data

Relevant AI Trustmark assurance

AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.

Methodology · How verification works

Products and providers

Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.

  • CoreWeave GPU Compute

    By CoreWeave

    CoreWeave is the force multiplier that empowers pioneers with momentum, magnitude, and mastery—enabling them to innovate with confidence. Explore the #1 AI Cloud. CoreWeave GPU Com

  • Crusoe Cloud

    By Crusoe

    Crusoe provides next-gen AI infrastructure and cloud compute using an energy-first approach. Deploy AI workloads at scale with reliable performance and 24/7 support. Crusoe Cloud i

  • CUDO Compute

    By CUDO Compute

    CUDO Compute designs, commissions and operates production AI infrastructure. We secure land, provision power and deploy NVIDIA GPU clusters to deliver environments engineered for l

  • Fluidstack AI Cloud

    By Fluidstack

    We build compute faster than anyone on the planet. Gigawatt data centers, built in America, delivered in months. We're hiring the people who build them. Fluidstack AI Cloud is

  • Graphcore

    By Graphcore

    Graphcore is a Bristol-origin semiconductor company known for Intelligence Processing Units for machine learning. Public materials cover AI compute hardware and software stacks for

  • Lambda GPU Cloud

    By Lambda

    Train and scale AI on NVIDIA VR200 NVL 72, GB300 NVL 72, B300, B200, H200, H100, and and more GPUs. Launch on-demand instances or reserve a cluster. Get started. Lambda GPU Cloud i

  • Modal

    By Modal

    Modal provides high-performance AI infrastructure for inference, training, batch jobs, and sandboxes with fast cold starts and autoscaling, defined in Python. Public marketing also

  • Nebius AI Cloud

    By Nebius

    Build and scale faster on the purpose-built AI cloud, engineered from silicon to API. Nebius AI Cloud is Nebius's platform, from Nebius B.V. Published at nebius.com/services/comput

  • NVIDIA DGX Cloud

    By NVIDIA

    NVIDIA now describes DGX Cloud as its internal cloud environment for building and operating AI at scale. It is used to develop open frontier and foundation models such as the NVIDI

  • OVHcloud AI

    By OVHcloud

    OVHcloud offers European public-cloud AI/GPU services for training and inference with strong data-sovereignty positioning. Public marketing also emphasises eU data residency/sovere

  • Runpod GPU Cloud

    By Runpod

    AI infrastructure with on-demand GPUs and serverless compute. Run training, inference, and batch workloads on the cloud with Runpod. Runpod GPU Cloud is Runpod's platform, from Run

  • Scaleway Generative APIs / GPU

    By Scaleway

    Scaleway provides European cloud GPU instances and generative AI APIs for model hosting and inference. Public marketing also emphasises european cloud + GenAI APIs. It is presented

  • Vast.ai GPU Cloud

    By Vast.ai

    Rent high-performance cloud GPUs at low cost with Vast.ai. Instantly deploy GPU rentals for AI, machine learning, deep learning, and rendering. Flexible pricing, fast setup, and gl

Products

Claimed products appear first. Ranking packs and payment do not change this list.

Related categories

Relevant procurement and assurance guides

Frequently asked questions

What is GPU / compute platforms?

GPU and accelerator capacity for training or inference, not a model product. Jobs: gpu vms; reserved clusters; multi-node training fabric.

What should not be listed as GPU / compute platforms?

Products whose buyer job is inference hosting stacks, training platforms, or generative-AI PaaS. Those belong on their own category page so search queries are not split.

Has AI Trustmark independently assessed every GPU / compute platforms supplier?

No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.

What are GPU / compute platforms?

GPU and accelerator capacity for training or inference, not a model product. Jobs: gpu vms; reserved clusters; multi-node training fabric.

What model or provider changes should a buyer insist on being told about?

Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.

How should buyers verify where AI customer data is processed?

Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.

Does a TrustMark on one product cover the rest of the company?

No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.

What incident-handling evidence is useful for AI suppliers?

Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.