The 5-step pipeline.
How energy turns into a deployed, financed, offtake-matched cluster, and what SLYD does at every step.
How SLYD works →Platform
SLYD Cloud
New and recovered GPU systems.
NVIDIA and AMD systems through documented manufacturer and qualified channel supply, with financing and deployment coordinated on the same platform.
Explore GPU hardware →By accelerator
Infrastructure and services
Compute, hardware, and power in one book.
Browse available GPU capacity by accelerator, configuration, region, and price, or bring supply to qualified demand.
Open marketplace →Compute
Bring supply
For buyers
Start with an indicative structure.
Tell us deal size, structure, and offtake. Any range is preliminary and subject to underwriting, diligence, and documentation.
Open Configure →For lenders
GPU market trends and deployment playbooks.
Infrastructure best practices, hardware comparisons, and industry analysis from the SLYD team.
Read the blog →Company
Plan the complete AI infrastructure stack
Accelerators are one decision inside a system. This page works through the rest of it: interconnect, storage, power, cooling, site readiness, deployment path, and the sourcing route that fits the requirement.
What is AI infrastructure?
AI infrastructure combines accelerated compute with the networking, storage, power, cooling, software, and operational controls required to run AI workloads reliably. SLYD helps organizations translate workload, scale, location, timeline, and commercial requirements into a system plan, then evaluate sourcing, financing, and deployment paths for the required components.
Start with the workload, not the product
The workload determines which parts of the system are actually under pressure. Naming it precisely is the difference between a design that scales and a rack of equipment that does not fit the job.
Pretraining and large-scale training
Bound by collective communication across many accelerators. Interconnect topology, bandwidth, congestion behavior, and job resiliency dominate the design.
Fine-tuning and post-training
Smaller node counts, shorter jobs, frequent iteration. Accelerator memory and fast checkpoint storage usually matter more than fabric scale.
Inference and serving
Bound by accelerator memory, latency target, and cost per request. Often scales as many independent replicas rather than one tightly coupled cluster.
Rendering and visualization
Graphics and media pipelines with different accelerator, display output, and software certification requirements than data-center training systems.
Simulation and technical computing
Often needs double-precision performance, deterministic scheduling, and parallel file system throughput that AI-first designs do not prioritize.
Mixed and shared estates
Multiple teams and job types on shared capacity. Partitioning, scheduling, and isolation requirements shape the system as much as raw performance.
Turn workload requirements into system requirements
Each workload property maps onto a concrete part of the system. Working through the mapping first keeps the conversation about requirements rather than product preference.
| Workload property | Primary system consequence | What to confirm |
|---|---|---|
| Model size and precision | Accelerator memory per device and per node | Whether the model, activations, and KV cache fit without excessive sharding |
| Parallelism strategy | Scale-up domain size and interconnect generation | How many accelerators must communicate as one tightly coupled domain |
| Cluster scale | Scale-out fabric, topology, and oversubscription | Switch tiers, cabling distances, and failure domains |
| Dataset and checkpoint size | Storage capacity, throughput, and data path | Sustained read and write rates during real job phases, not peak specifications |
| Latency and concurrency target | Replica count, batching design, and placement | Whether the target is achievable on the intended accelerator and software stack |
| Utilization profile | Own, colocate, or rent decision | Expected sustained utilization over the asset life |
| Data control requirement | Deployment location and tenancy model | Where data may reside and who may operate the environment |
The site decides what is deployable
High-density accelerated systems are usually constrained by the building before they are constrained by the budget. These four dependencies are worth checking before a system is selected, not after.
Utility capacity and distribution
Available capacity, redundancy target, distribution to the rack, protection, metering, and commissioning.
CoolingThermal capacity and method
Whether the site supports the server's approved cooling design, including facility water where liquid cooling is involved.
NetworkFabric and cabling paths
Scale-up and scale-out fabrics, storage and management networks, optics, and physical cable routes and distances.
FacilitySpace, structure, and access
Rack footprint, floor loading, delivery and rigging access, containment, security, and operational support model.
Compare where the system will live
| Path | Fits when | Main constraint to test |
|---|---|---|
| On premises | Data control, existing facility capacity, and sustained utilization justify ownership | Whether the building can actually take the power, thermal, and structural load |
| Colocation | Ownership is wanted without operating a facility | Available density per rack, cooling method supported, and contract term against refresh cycle |
| Cloud or marketplace capacity | Demand is variable, short-lived, or still being characterized | Cost at expected sustained utilization and data residency requirements |
| Hybrid | A predictable base load sits alongside variable demand | Operational complexity of running two environments and moving data between them |
If the requirement is compute rather than owned equipment, the GPU Cloud Marketplace is the path to evaluate first.
Accelerator and manufacturer families
Each page below covers one family, with the manufacturer's own current specifications and the system questions that go with it. Full side-by-side specifications live in the GPU database. Product names are descriptive and do not indicate availability.
Accelerator families
Manufacturer families
Build the requirements package
Start with whatever is known. Gaps are normal, and the unknowns are usually the first thing worth working on together.
- Workload and scale
- Target systems, if already selected
- Site or colocation location
- Existing utility and cooling capacity
- Target deployment date
- Redundancy requirement
- Growth plan
- Known constraints, including capital structure
What is verified before anything is represented
Price, condition, availability, warranty, and lead time change by product, supplier, geography, and transaction. None of them are page content. They are confirmed for the specific equipment and quantity, with the confirmation date and scope stated.
Requirement
Workload, architecture, quantity, location, timing, budget, condition tolerance, and deployment constraints are captured before supply is evaluated.
Supply qualification
Configuration, seller authority, provenance, condition, availability, commercial terms, and applicable compliance requirements are reviewed for the specific transaction.
Whole-transaction comparison
Equipment cost is evaluated together with networking, storage, power, cooling, logistics, warranty, financing, and deployment implications.
Buyer decision
The buyer reviews the specific quote, evidence, counterparties, and governing terms before any transaction proceeds.
Product names on SLYD pages are descriptive. They are not statements of stock, allocation, authorization, or availability.
Related paths and resources
AI infrastructure questions
What is AI infrastructure?
AI infrastructure is accelerated compute plus the networking, storage, power, cooling, software, and operational controls required to run AI workloads reliably. Treating the accelerator as the whole system is the most common planning error, because the surrounding infrastructure usually decides what can actually be deployed at a given site.
How do I size AI infrastructure for my workload?
Start from the workload rather than the product. Model size, precision, context length, dataset size, concurrency, and latency target drive accelerator memory and interconnect requirements. Those requirements then drive server form factor, rack density, network fabric, storage throughput, power draw, and cooling method, which are constrained in turn by the site.
What is the difference between training and inference infrastructure?
Training is usually bound by collective communication across many accelerators, so it is sensitive to interconnect bandwidth, topology, and job resiliency. Inference is usually bound by accelerator memory, latency, and cost per request, and often scales as many smaller independent replicas. The same accelerator can suit both, but the surrounding network, storage, and redundancy design differs.
Should I deploy on premises, in colocation, or in the cloud?
It depends on utilization, data control, capital structure, timeline, and available site capacity. Sustained high utilization and strict data control favor owned systems. Variable or short-lived demand favors rented capacity. Many organizations combine both, owning a base of capacity and renting for peaks or experiments.
What power and cooling capacity does a GPU deployment need?
It is configuration specific. The requirement follows from the exact server model, accelerator count and power profile, rack layout, redundancy target, and facility conditions. A design that works in one building can be undeployable in another with the same equipment, so utility capacity, thermal capacity, floor loading, and cabling paths are checked before a system is selected.
Does a product named on this page mean SLYD has it available?
No. Product and platform names are used descriptively to explain architecture choices. Availability, condition, price, lead time, and warranty are confirmed at the transaction level for the specific configuration, quantity, and geography requested.
What does SLYD verify before representing a system?
Configuration, seller authority, provenance, condition, availability, commercial terms, and applicable compliance requirements are reviewed for the specific transaction. Testing, warranty, and condition evidence are disclosed for the specific equipment when they exist rather than asserted as a blanket page claim.
What should I have ready to start an infrastructure conversation?
Workload and scale, target systems if already chosen, site or colocation location, existing utility and cooling capacity, target deployment date, redundancy requirement, growth plan, and known constraints. Incomplete information is normal at first contact, and the unknowns themselves shape the first round of work.
Work through an infrastructure requirement
Share the workload, scale, location, timeline, and constraints. That requirement drives the system plan and which sourcing, financing, and deployment paths are evaluated.
Page updated: August 18, 2026