GPU solutions for
every workload
Build, train, and deploy across compute, storage, networking, and serving without the lock-in.
What are you building?
Fine-tuningThe problem
GPU access is broken. We fixed the market.
Compute is the scarcest resource in AI, yet most of the world's GPUs sit idle. Kracht connects both sides and sets a fair price in real time.
Renting GPUs today
- H100s sold out, with waitlists before you can train
- Hyperscaler rates above the silicon's worth, with long lock-ins
- Egress fees and opaque minimums you only find on the invoice
- Quota requests and sales calls just to get started
- Your own GPUs sit idle, depreciating, earning nothing
A two-sided compute market
- Thousands of listed GPUs in preview, with launch flow under 20 seconds
- Market-set prices, often ~70% lower for the exact same card
- Per-second billing, no egress, no minimums, no lock-in
- Sign up and kracht launch, with no tickets or gatekeeping
- List unused GPUs and turn idle hardware into income
By workload
However you train, serve, or scale
Accelerated compute focused on simplicity, speed, and price, matched to the way your team actually works.
Illustrative. Sample figures, not live marketplace metrics
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From idea to running in four steps
Pick your GPU
selectBrowse the marketplace preview and choose the accelerator that fits, from H200 to RTX 4090, at a market-set price.
Launch in seconds
launchBring your own Docker image and start with one command from the CLI, API, or dashboard. No tickets or setup queue.
Scale on demand
scaleBurst from a single GPU to a multi-node cluster with fast interconnect, then scale back down when the run finishes.
Pay per second
settleBilling follows only the seconds you run. Stop the instance and billing stops with it.
Why Kracht
The outcomes teams actually want
Ship faster
Capacity in seconds, not procurement cycles. No tickets, just compute when you need it.
Spend less
Market-driven, per-second pricing can cut hyperscaler rates for the same silicon.
Scale cleanly
Go from one GPU to a 1,000-GPU cluster and back down as the workload changes.
Stay in control
Bring your tools, keep data isolated, and avoid getting locked into a runtime.
Global by default
One network, 40+ regions
Schedule workloads close to your data and users. Capacity is pooled across providers worldwide and routed where it is cheapest and fastest.
Example scenario
A sample workload outcome
“We moved our training off a hyperscaler and onto Kracht. Same H100s, a fraction of the cost, and no capacity queue.”
Ready to scale your
compute?
Launch a GPU in seconds, or list your idle hardware and start earning, settled per second, no lock-in.