Clear the backlog overnight.
Spread offline inference, embeddings and evals across many GPUs at once and get through the queue faster.
Batch inference · Embeddings · EvalsKinesis Batches
GPUs by the hour for rendering, AI and mining. Your Kinesis Grid keeps the work running as GPUs come and go.
Your Kinesis Grid
53 GPU models65 online now130 online this week
Each square is a GPU that was online in the last 7 days. They come and go through the day, and your batch spreads across whichever are online.
Every price below is a cap: the most you'll pay per GPU-hour. No bidding, no surprises. Add GPUs to build your grid and see the most your run can cost.
Live · updated Nov 10, 3:54 PM UTC
Individual GPUs come and go through the day, and your Kinesis Grid replaces any that leave with the next to come online. Add any GPU to your grid: it joins as soon as a GPU of that model is free.
Up to, per machine-hour
Up to, per machine-hour
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Add GPUs from the list and your grid takes shape here. Each GPU runs on its own machine, and the totals show the most your run can cost.
Per GPU, per hour. Listed prices are caps: you never pay more than this. Made for work that runs in batches. Prices as of November 10, 2026.
Dozens of GPUs, a mix of models or a long run? Tell us what you're working on and our team will get back to you with what's available and a price.
Prefer email? sales@kinesis.network
Takes two minutes. We'll match you with the right hardware.
If your work runs in batches, pay only for the hours it runs.
Spread offline inference, embeddings and evals across many GPUs at once and get through the queue faster.
Batch inference · Embeddings · EvalsSend every frame to its own GPU. Animations, product shots and VFX finish sooner, and your budget goes further.
Animation · Product shots · VFXRun LoRA fine-tunes and hyperparameter sweeps, one experiment per GPU, for the same budget. Save checkpoints to your own storage and pick up where you left off.
Fine-tuning · LoRA · ExperimentsRunning a website, database or live API that has to stay up around the clock? Kinesis runs those too, on dedicated compute with your private Grid included, from small CPU machines to 8-GPU H100 nodes. Spot is built for work that runs in batches. See dedicated compute →
Choose models and see exactly what they cost per hour, before you sign up.
Create an account with Google, GitHub or email, add a card and launch.
Results and checkpoints go straight to your own storage, so every GPU-hour counts.
Capacity that would otherwise sit idle. You pay by the hour, never more than the model's listed cap. Any single machine can leave without notice when its owner needs it, but your Kinesis Grid replaces it with the next GPU that comes online, so your batch keeps running on one steady fleet.
Your Kinesis Grid's control plane replaces it with the next machine that comes online and keeps your work running, so you never manage single GPUs. Any one GPU may stay online for only a few hours, so save progress (checkpoints) to your own storage as you go: the replacement then picks up where the work left off instead of starting over.
Each GPU model has a maximum hourly rate, shown in the price list. That's a cap: you never pay more than it. No bidding. Planning a large or long run? Talk to our team.
Our GPUs live in machines their owners also use, so what's free changes through the day. “Online now” counts the GPUs of that model online this minute, free or already running someone's work; “online this week” counts the ones that were online at some point in the last 7 days. Add any GPU to your grid whenever you like: your Kinesis Grid brings one in as soon as a GPU of that model is free, and replaces any that leave.
Anything that runs in batches: offline inference, embeddings, rendering, fine-tuning and experiments. If it runs in a container on a single GPU, it runs here.
Each machine is its own GPU, so the best fit is work you can split across many of them. Planning something multi-GPU? Talk to our team.
Sign up with Google, GitHub or email and deploy to spot GPUs in minutes. Every hourly price is a cap.