Find GPU compute capacity
Compare current provider-supplied listings by accelerator, configuration, region, tenancy, availability, pricing basis, software environment, and commercial terms.
Availability and deployment are listing-specific and remain subject to confirmation and the applicable terms.
Available Servers
Provider-supplied listings as currently published. Status, price, configuration, and location are shown as the provider supplied them and remain subject to confirmation through the listing's own transaction path.
1x RTX A6000
Professional Workstation
1x RTX A6000
Professional Workstation
1x RTX A6000
Professional Workstation
1x RTX A6000
Professional Workstation
1x RTX A6000
Professional Workstation
1x RTX 6000 ADA
AI Inference & Visualization
1x L40
General Purpose GPU
1x L40
General Purpose GPU
1x L40S
General Purpose GPU
1x RTX A6000
Professional Workstation
1x RTX PRO 4500 Blackwell
General Purpose GPU
1x RTX A6000
Professional Workstation
No current listing fits your configuration, scale, or timing?
Contact SalesFrom comparison to confirmed capacity
Compare current listings
Review the published listings by accelerator, node configuration, region, availability, and rate.
Review listing-specific configuration and terms
Check the tenancy, software environment, networking, storage, support scope, billing basis, and fees that apply to that listing.
Confirm identity, organization, end use, and geography
Sign in so identity and organisation are established, and complete the checks the listing and provider require.
Request, reserve, or deploy through the supported path
The transaction path available for that listing determines whether capacity is requested, reserved, or provisioned directly.
Track confirmation, provisioning, and operating status
Follow the capacity through confirmation and provisioning to running state in your account.
What is the SLYD GPU Cloud Marketplace?
It is a marketplace for comparing current GPU compute listings from approved providers. Each listing defines its accelerator and node configuration, region, tenancy, availability, pricing basis, software environment, support, and transaction path. Buyers can inspect current listings or submit a private requirement when no listing fits.
Configuration, software, networking, support, billing basis, and deployment path belong to the individual listing. They do not carry across from one listing to another, and nothing on this page adds a term to a provider's offer.
What teams run on rented capacity
Model training
Training foundation models, fine-tuning, and hyperparameter sweeps. Multi-GPU and multi-node work depends on the topology and interconnect the listing publishes.
- Node topology and GPU count per node
- Interconnect and network bandwidth
- Storage for checkpoints, where the listing offers it
Inference
Serving production endpoints. Which serving stack is available, and whether scaling is managed or your own responsibility, is set by the listing.
- Accelerator choice against cost per query
- Region, for latency to your users
- Whether the image is supplied or you bring your own
Development and experimentation
Notebook and IDE work with GPU access for prototyping and research, usually on shorter and less predictable cycles.
- Shorter durations and smaller configurations
- Environment supplied by the listing or by your container
- Whether workspaces persist between sessions
Data processing
GPU-accelerated preprocessing, feature engineering, and ETL, where throughput and data locality usually matter more than peak accelerator performance.
- Local and shared storage on the listing
- Network bandwidth and egress charges
- Region, relative to where the data already sits
When to rent cloud GPUs
Rent cloud GPUs
- Variable or unpredictable workloads
- Short-term projects or experiments
- Capacity needs that change between projects
- Avoiding upfront capital expenditure
- Access to accelerator generations you do not own
- No in-house team to run hardware
Buy hardware
- Consistent long-running utilisation
- Multi-year projects
- Data residency requirements
- Predictable capacity needs
- On-premises requirements
- Lower long-term cost of ownership
Owning the deployment instead? Source physical GPU hardware, finance the infrastructure, or configure an owned deployment. Not sure which route fits? Talk to our team.
Renting GPU compute instead of owning it
Renting shifts GPU capacity from a capital purchase to an operating cost, which suits variable, short-term, or exploratory work. What you get in return, including the software environment, networking, support, and billing basis, is defined by the listing you select rather than by the marketplace itself.
Accelerators that commonly appear
Provider listings change, so treat the cards above as the current answer. The accelerators below are the ones buyers most often compare, described by their published hardware characteristics rather than by any availability or price claim.
H200 SXM
141GB HBM3e and 4.8 TB/s of memory bandwidth. Suits large-model training and memory-bound inference.
H100 SXM
80GB HBM3 and 3.35 TB/s of memory bandwidth. The common reference point for AI training and fine-tuning.
A100
40GB or 80GB HBM2e. Established performance for training and inference at a lower rate.
RTX 6000 Blackwell Pro
Professional accelerator used for inference, visualization, and mixed professional workloads.
L4 and A10
Lower-cost inference accelerators for production serving where throughput per dollar matters more than peak performance.
Full specifications for these and other accelerators are in the SLYD GPU database.
Selection guide
Match the accelerator to the workload, then check whether a current listing actually offers it in the configuration, region, and tenancy you need.
For training
H100 and H200 give the highest throughput for large models. Multi-GPU work depends on the node topology and interconnect published on the specific listing.
For inference
A100, L4, and RTX 6000 often give better cost per query. Serving frameworks and images vary by listing, so confirm what the provider supplies and what you bring.
For development
Most accelerators work for prototyping. Starting on lower-cost capacity and moving up once the workload is understood keeps early spend down.
For larger scale
Multi-node work depends on available quantity, interconnect, and provider capacity. Where current listings do not cover it, post a requirement instead of assuming scale is on tap.
Reading the price
A rate is only comparable once you know its basis. Check these on the listing before comparing two cards against each other:
Billing basis and minimum
The unit the rate is charged in, and any minimum duration or commitment attached to it.
What the rate covers
Which of compute, storage, network, egress, and support sit inside the quoted rate, and which are charged separately.
Terms and taxes
Setup fees, applicable taxes, and the reservation, cancellation, and renewal rules for that listing.
Who does what
Roles differ by transaction, and the terms attached to a listing are the ones that govern it:
- SLYD: operates the marketplace, publishes approved listings, and coordinates the transaction workflow
- The provider: supplies the capacity and sets the configuration, software environment, support scope, and service terms for its listing
- Listing terms: define tenancy, networking, storage, billing basis, fees, and the deployment path that actually applies
- Nothing is inferred: software, support, networking, SLA, and billing are never carried across from one listing to another
Need more control?
For consistent long-running utilisation, owning hardware may offer better economics than renting.
Browse HardwareFrequently Asked Questions
What is a GPU cloud marketplace?
It is a place to compare current GPU compute listings from approved providers in one view. Each listing defines its own accelerator and node configuration, region, availability, pricing basis, software environment, support, and transaction path, so buyers can compare real capacity rather than generic marketing.
Which GPU models can appear in the SLYD marketplace?
Whichever models providers currently list. Enterprise NVIDIA accelerators such as H200, H100, and A100 appear regularly, alongside inference and professional cards. The listings shown on this page are the live set at the moment you load it, so treat the visible cards as the answer rather than any fixed model list.
How current are availability and prices?
Availability status and price are supplied by the provider for that listing and are shown as provided. They can change between the moment a listing is published and the moment capacity is confirmed, so a status shown here is an indication to act on, not a guarantee. Confirmation happens through the transaction path for the specific listing.
Does every listing support instant deployment?
No. Deployment path and lead time are listing-specific. Some capacity can be provisioned through a self-service flow, and other capacity requires reservation, provider confirmation, or account and compliance setup first. Check the terms attached to the specific listing rather than assuming a single platform-wide deployment time.
What fees can apply beyond the displayed compute price?
Depending on the listing, charges may include setup, storage, network and egress, support tiers, and applicable taxes, and a minimum duration may apply. The rate shown on a card is the compute price for that listing on its stated billing basis. Confirm the full commercial terms before reserving or deploying.
How do tenancy, networking, storage, and software vary by listing?
They vary a great deal. Tenancy can be shared or dedicated, networking and interconnect differ by provider and node topology, storage may be local, shared, or charged separately, and the software environment can be a base image, a container you supply, or a managed service. None of these carry across from one listing to another.
Who provides and supports the compute service?
Capacity is supplied by the provider on the listing, and support scope and response terms are set by that provider unless the listing states otherwise. SLYD operates the marketplace and coordinates the transaction workflow. The provider, billing, and support roles that apply to a given transaction are the ones stated in its terms.
What happens if no current listing fits?
Post a private compute requirement with the accelerator, scale, region, timing, tenancy, software, budget, and commercial constraints. SLYD reviews it against current and forward supply rather than leaving you to re-check the public listings, and follows up through the requirement workflow.
What checks occur before capacity is reserved or deployed?
Signing in establishes identity and organisation, and the transaction path confirms the details required for that listing, which can include end use, geography, export and acceptable-use constraints, and the provider's own onboarding requirements. Those checks happen before capacity is confirmed, not after.
Can I buy physical hardware instead of renting compute?
Yes. SLYD sources physical GPU servers and supporting infrastructure, and can arrange financing for an owned deployment. Buying tends to suit consistent long-running utilisation, data-residency requirements, and predictable capacity, where renting suits variable or short-term workloads.
Compare current capacity
Browse the listings published right now, or post a private requirement when none of them fits your configuration, scale, region, or timing.