Dedicated compute.
Your private Grid included.
Get the compute your workloads need, supplied through Kinesis and operated as your own private Grid. Deploy applications, share resources across your teams, and add capacity while Kinesis handles placement, scaling, and recovery.
Find the compute your workload needs.
Start with the work you need to run. Choose capacity around its processing, memory, location, and availability requirements, with Kinesis helping source the right configuration.
Processing
The card class and count the job calls for.
Training, fine-tuning, inference, and batch work each want different hardware. Pick the class and the count the work needs, not the one that happens to be free.
Memory
Enough for the model, not for the guess.
Weights, context length, and batch size set the floor. Size the card to the model you run so it fits without splitting or spilling to host memory.
Location
Where the data is allowed to be.
Name the country or region up front. Kinesis places the work inside that line, and nowhere else.
Availability
Held for the deadline, open for the rest.
Some work needs capacity waiting for it. Some can take the next gap. Say which, and the configuration follows.
Tell Kinesis what the work needs. We source the configuration from servers you own, capacity you already reserved, or compute we supply, and all of it lands in your Grid.
Your compute comes with the platform to run it.
Every Kinesis compute deployment includes your own private Grid: one way to deploy workloads, manage access, and operate the capacity supplied to your team.
Deploy your applications.
Bring code, containers, or templates into one application deployment experience.
Put capacity to work.
Share resources across approved users, allocate GPU fractions, and reclaim idle sessions.
Keep workloads operating.
Kinesis handles placement, scaling, load balancing, and recovery.
Stay in control.
Set resource limits, govern access, and separate production services from training workloads.
The Grid is included in the quoted compute price.
Dedicated to your team. Shared on your terms.
Kinesis supplies hardware reserved for your organization or a group you choose. Your administrator decides who can share it and how resources are allocated.
Right-size allocations. Give lighter workloads isolated GPU fractions so more users can work on the supplied capacity.
Control consumption. Set resource limits and GPU-hour budgets, queue work by fair share, and reclaim idle sessions.
Protect production. Separate live services from training and experimentation so they do not compete for the same memory.
Get more useful work from the capacity you pay for, with control over who uses it.
From compute selection to running workloads.
Choose your capacity.
Select a Kinesis configuration or discuss the requirements for the capacity you need.
Deploy your workload.
Bring an application, container, or template into your private Grid.
Set access and priorities.
Define who can use the capacity and what matters to the work: cost, reliability, latency, or provider independence.
Run and operate.
Kinesis places and operates the workloads across the capacity supplied to your Grid.
Deploy from
Start with Kinesis. Grow the Grid with your own infrastructure.
Begin on compute Kinesis supplies, with your private Grid in place from the first workload. As the Grid proves its value, connect the servers, cloud accounts, and colocation you already own to the same Grid, and deploy and manage every workload the same way.
Bring your infrastructure in when you are ready, at your own pace. Yours and ours mix in any amount and any order, and Kinesis-supplied compute stays available for peaks and for anything you would rather not buy.
Tell us what you need to run.
Share your workload and compute requirements. We’ll help you identify a Kinesis configuration and the next step to getting it running.