Know what to buy before you buy it.
An infrastructure assessment turns your intended AI workloads and data constraints into a deployment model, a hardware specification, and an operating plan. You get documents you can review internally, not a sales quote dressed as advice.
How an assessment runs.
- 1
Discover
Interviews with application owners, security, and infrastructure teams to document use cases, data, and constraints.
- 2
Profile workloads
Estimate users, request patterns, context lengths, and batch volumes for each use case.
- 3
Test candidates
Run shortlisted open-weight models on representative prompts to set quality and precision requirements.
- 4
Recommend
Deliver a deployment model, hardware specification, network design, and operating plan.
The questions behind the hardware.
| Area | What we look at | Why |
|---|---|---|
| Models | Size, precision, and number of models served | Sets the GPU memory floor |
| Concurrency | Peak simultaneous users and applications | Sets cache and GPU count |
| Context | Typical and maximum prompt length, especially for RAG | Drives memory per request |
| Batch work | Volumes and deadlines for offline jobs | May justify separate capacity |
| Data constraints | Classifications, residency, connectivity | Determines deployment model |
| Facility | Power, cooling, rack space, network | Determines on-prem feasibility |
| Operations | Team skills and availability | Self-run or managed services |
What you receive.
Workload profile
Documented use cases with usage estimates and assumptions stated explicitly.
Model recommendations
Candidate models with evaluation notes and the precision each requires.
Architecture
Deployment model, network placement, and integration points with your systems.
Hardware specification
A bill of materials sized to the profile, with headroom and growth options.
Operating plan
Responsibilities for patching, monitoring, backup, and model updates.
Risks and open questions
What could change the recommendation and how to test it.
Use the results however you like.
The specification is yours. You can buy hardware through Data.co, through your existing vendors, or use what you already have where it fits. We would rather you buy the right amount of hardware than more of it.
If you already own GPUs, the assessment checks them against the workload profile and recommends additions only where needed.
What a workload profile entry looks like.
Each use case is captured with explicit assumptions, so anyone reviewing the sizing can see what would change it. The values below are illustrative placeholders, not a recommendation.
use_case: claims-summaries
owner: claims-operations
users:
named: <count>
peak_concurrent: <count> # assumption: weekday mornings
traffic: interactive
context:
typical_tokens: <n>
max_tokens: <n> # longest claim packet tested
data:
datasets: [claims, policy-documents]
classifications: [PHI, internal]
candidate_models: [general-chat]
precision_tested: [fp16, fp8]
open_questions:
- Will attachments be OCR'd locally or excluded?Illustrative example · final API surface documented in /docs
What helps an assessment move quickly.
- A short list of candidate use cases with a named owner for each
- Rough user counts and when usage peaks
- The data sources involved and their classifications
- Any residency, connectivity, or contractual constraints
- An inventory of existing GPUs and spare data center capacity
- Sample documents or prompts that are approved for testing
- Who will operate the environment after go-live
Request an infrastructure assessment
Tell us about the workloads, data and constraints. We scope capacity and architecture before recommending anything to buy.
Questions buyers ask
How long does an assessment take?
It depends on the number of use cases and stakeholders. Scope and timeline are agreed at the start, based on how much discovery and model testing your situation needs.
Do we need to share sensitive data?
No. Workload profiling uses descriptions and synthetic or approved sample prompts. Model testing can run inside your environment if real samples are needed.
Are we committed to buying from Data.co afterward?
No. The deliverables are yours to use with any vendor.
Can the assessment cover existing hardware?
Yes. We inventory existing servers and GPUs and assess whether they can serve some or all of the workloads.
Will you give us performance guarantees?
We give measured results from the testing we run and clearly stated assumptions. Real performance depends on traffic and configuration, so we document how to validate it after deployment.
Who should be involved from our side?
An executive sponsor, the owners of the first use cases, someone from security or compliance who knows the data classifications, and an infrastructure lead who knows the facility, network, and existing hardware.
What if our use cases are still vague?
That is common. Discovery narrows them to a few concrete workloads with owners, and the profile states the assumptions explicitly, so the plan can be revisited as usage becomes clearer.
Start with the workloads, not the hardware.
Tell us the use cases you are considering and the constraints you work under. We will scope an assessment around them.