Products & resources
Plan the compute. Put your models in the right environment.
Request resource pricing ↗01 / Workload
Start with the job the GPU needs to do. Configuration and capacity follow from a representative workload.
Start with concurrent users, request lengths and response-time requirements. Test representative requests before selecting baseline capacity and an approach to peak demand.
Inputs for sizing
Identify the base model, dataset size and expected job duration. Plan the tuning job, output storage and subsequent inference separately, so a one-off task does not become an unnecessary recurring compute cost.
Inputs for sizing
Assess inter-GPU communication, data loading and checkpoint storage alongside memory and GPU count. Establish the model licence and workload before deciding whether a cluster is appropriate.
Inputs for sizing
Specify the cloud or supplier, resource configuration, account arrangement and term in the proposal. Assess single- or multi-GPU options and dedicated or shared capacity against the project’s requirements and available supply.
02 / Deployment path
A private connection and a model running on your own GPU are different arrangements. Compare the operating boundary before choosing a path.
Provider environment
Boundary illustration. Availability, licensing and implementation scope require project-specific confirmation.
Check whether the target service supports the required private connection. The provider still runs the model. Review processing location, logging and retention; a private endpoint does not mean data stays entirely within your environment.
Your cloud account / VPC
Boundary illustration. Availability, licensing and implementation scope require project-specific confirmation.
For models licensed for self-hosting, plan GPU resources, serving, storage and access controls within your account. Review workload data, management connections and external dependencies separately.
Your GPU equipment
Boundary illustration. Availability, licensing and implementation scope require project-specific confirmation.
Assess existing servers, drivers, networking and storage. Agree who handles model acquisition and licensing, image delivery, administrator access and any on-site work.
Validated restricted environment
Boundary illustration. Availability, licensing and implementation scope require project-specific confirmation.
Review model and software licences, offline installation, licence checks and update dependencies first. Include offline operation in scope only after the full data path and external dependencies have been validated against the requirement.
Private connectivity, deployment in your cloud account and deployment on your own equipment have different scopes. A hosted model API does not grant downloadable model weights. The available approach depends on licensing, region, resource availability and the agreed project scope.
03 / Define the handover
Agree the scope before implementation. Each stage has a decision and a record to hand over.
Review existing cloud accounts, applications, documents or datasets, permissions and network restrictions. Establish business goals, budget and data-handling requirements using sanitised material, without requesting production keys.
Specify the resource source, region, configuration, account ownership, implementation scope, cost components and appropriate contracting entity. Compare ongoing operating costs as well as resource charges.
Agree test samples, output quality, concurrency, latency, failure recovery and usage reconciliation. Proceed against agreed acceptance criteria; revise the plan or stop that stage if the criteria are not met.
Hand over the agreed configuration, versions, access inventory, billing basis and operating instructions. For an exit or migration, separately agree data export, access revocation, resource release and retention obligations.
See what the quotation contains.
Resource charges and delivery work are separate cost components. Confirm the applicable items and billing terms in the proposal.
Agree a comparable, costed proposal before arranging resources and implementation. Assessment, pilot, launch and ongoing support can be scoped and purchased as separate stages.
Open the relevant topic when you need the implementation and acceptance requirements.
Size GPU resources around model size, memory needs and workload.
Plan around model licences, networking and your existing systems.
Define validation, operating responsibilities and handover materials.
Define resources, access controls and data handling boundaries.
Connect model capabilities with existing applications and workflows.
Assess new models, workload changes and resource adjustments.
Assess GPU capacity and deployment scope for the task. Confirm specific configurations, regions and supply conditions in the proposal.
Distinguish inference, fine-tuning and training. Provide model licensing, parameter count, precision, context length, concurrency and sample workloads to estimate memory, compute and scaling needs rather than purchasing on GPU model alone.
Specify driver, CUDA, framework and inference server versions, plus images, storage, networking and data loading. Agree resource ownership, administrator access, monitoring and backup responsibilities.
Knowledge retrieval and agents access business data and systems. Agree access boundaries and acceptance samples before deployment.
For RAG, define document sources, refresh cycles, retrieval permissions and citations. For agents, define allowed tools, human approval and failure recovery. Model capabilities do not replace access controls.
Validate quality, latency, capacity and failure handling against agreed samples. Hand over configuration records, access inventories, monitoring and recovery instructions. Ongoing coverage, change responsibilities and fees need separate agreement.
Products, regions and delivery terms follow the confirmed proposal and agreement.
Choose a service stage around the decision or delivery work you need.
