GPU on Swedish soil. H100 and L40S.
Our own hardware in our own rack — 6× NVIDIA L40S, 8× H100 80 GB as individual cards, and an HGX baseboard with 8× NVIDIA Delta-Next H100 80 GB. Single-tenant, the whole card is yours, and your model weights never leave Sweden.
Two cards. Three ways to book them.
Everything below sits physically in the rack in Falkenberg. No waitlist, no overselling, no shared capacity — book a card and it is yours alone.
NVIDIA L40S
The efficiency pick. Inference at scale, RAG, LoRA fine-tuning, vision and GPU rendering — at a fraction of the H100 hourly price.
6 cards · own hardware · live now To the L40S page →NVIDIA H100 80 GB
The training card. Eight individual cards for single-GPU jobs, plus an HGX baseboard with 8× Delta-Next H100 on an NVSwitch fabric for real training runs.
8 cards + HGX node · own hardware · live now To the H100 page →Per-card and per-node prices are on each page, or together under GPU pricing. The commit discount of up to 40% applies to GPU too.
Which card do you actually need?
Most people buy too big. If the model fits in 48 GB and you serve requests rather than train from scratch, the L40S wins on cost per token. Take the H100 when memory bandwidth or multi-GPU scale is the bottleneck.
Your models and your data stay in Sweden.
Training a model means condensing your company's know-how into a few hundred gigabytes. Where those files sit is a trade secret — not a triviality.
Kepler is a Swedish company with its own hardware in Sweden. No US Cloud Act, no foreign jurisdiction, no hyperscaler under the layer. The GPU fleet sits on the same EU soil as the rest of the platform.
Per-tenant isolation
Your own project, your own credentials via OpenBao.
The whole card, never shared
No vGPU partitioning, no neighbor stealing bandwidth.
No egress fee
Move datasets in and checkpoints out without a surprise invoice.
Ready to run, not ready to be configured.
CUDA 12.6 & PyTorch Now
Ubuntu 24.04 with CUDA, cuDNN, NCCL, PyTorch and Docker preinstalled — or boot your own image.
Root SSH & API
Bare metal or container. The same REST API and Terraform provider as the rest of the platform.
Local NVMe scratch
Fast disk right next to the card so your dataloader isn't the bottleneck. Persistent volumes for datasets you keep.
Unmetered traffic
Inbound always free, outbound with no egress fee on GPU nodes. InfiniBand on HGX when needed.
Engineers who run GPUs
Driver trouble, NCCL tuning, thermals — you reach someone who has been inside the rack.
Commit & save −40%
Per hour with a monthly cap, or lock 3–36 months and lower the price predictably without negotiation.
GPUs that sit in Sweden.
Tell us what you train or serve, and we come back with card, price and start date — usually the same day.