Jupyter on a dedicated cloud GPU

A Jupyter notebook GPU cloud, without the queue

Hosted notebooks are how most ML work starts — and where a lot of it stalls. Free and shared tiers hand you a fractional GPU behind opaque usage caps: sessions disconnect mid-epoch, runtimes get throttled when demand spikes, and you can never quite tell which accelerator you'll be assigned today. The moment your experiments take hours instead of minutes, the shared-runtime model works against you.

EponEdge takes the opposite approach. Every instance is a dedicated GPU — an NVIDIA RTX 3090 with 24 GB of GDDR6X, an RTX 5090 with 32 GB of GDDR7, or an RTX PRO 6000 Blackwell with 96 GB — reserved for you alone for as long as the instance runs. JupyterLab is included on every instance alongside root SSH, so you can start in a notebook and drop to a terminal to install CUDA libraries, pin package versions or run background jobs, with nothing resetting underneath you.

An EU-hosted Colab alternative for serious work

Because the notebook server runs on the instance rather than in your browser session, closing the laptop doesn't kill your training run. Reconnect from anywhere and the cell is still executing. There are no session timeouts and no compute-unit meters — just a machine that is yours until you stop it.

For students, PhD researchers and EU-funded projects there is a quieter advantage: the GPU sits in a renewable-powered micro-datacenter in Belgium, operated by a European-owned company headquartered in Brussels. Research datasets — medical, social-science, anything under an ethics-board agreement — stay under EU jurisdiction, GDPR-native, with no exposure to foreign cloud acts. That makes a compliance conversation with a university DPO dramatically shorter than "we uploaded it to a US notebook service."

Per-second billing fits how notebooks are actually used

Notebook work is bursty: twenty minutes of experimentation, an hour of training, a day of nothing. Hourly rounding punishes that pattern; subscriptions punish it worse. EponEdge bills per second on prepaid credit with no commitments — a fifteen-minute experiment on the Flexible tier costs about five cents. Stop the instance when you're done and billing stops with it. Persistent storage at $0.15/GB/mo keeps your environment, checkpoints and datasets ready for next time, so "spin up where I left off" takes minutes, not a morning of pip installs.

Honest fit: what one 3090 can and can't do

A single 24 GB GPU is a sweet spot for research and coursework: comfortable inference on 7B–13B language models, QLoRA fine-tuning up to roughly 13B, LoRA around 7B, and smooth Stable Diffusion, SDXL and Flux image generation — and when 24 GB is the constraint, the 32 GB RTX 5090 and 96 GB RTX PRO 6000 fit larger models on the same per-second terms. A 3090 is not the machine for 70B-parameter training or H100-class distributed jobs — if that's your scale, talk to sales about multi-node clusters, or look elsewhere without hard feelings. For everything the 24 GB class covers, see the full RTX 3090 page and pricing details.