NVIDIA Tesla P40 · 24GB VRAM

Сервер за записници на Jupyter GPU

Извршете ги Jupyter тетратките со посветена поддршка на NVIDIA GPU. Интерактивна наука за податоци и развој на ML со забрзување на CIDA.

Вклучи го GPU- серверот Сите опции на GPU

$ pip install jupyterlab torch && jupyter lab --ip=0.0.0.0 --allow-root
# Трчање на NVIDIA Tesla P40 (24GB)
Подготвени. _

Што е {име} на GPU VPS?

Јупитер тетратки на GPU VPS ви дава интерактивна развојна околина со оддадена NVIDIA GPU хардвер. Совршена за наука за податоци, ML експериментирање и модел прототипирање.

Зошто {име} на VPS.org ГПУ

GPU во белешки

Пристапи директно од клетките на тетратките.

ЈупитерЛаб

Целосно JupyterLab IDE со терминал, прелистувач на датотеки и наставка.

Претходно инсталирани библиотеки

ПиТорч, Тенсорфлоу, Скикит-учител, панди, и поподготвен за користење.

Оддалечен пристап

Дојдете до вашите тетратки од било кој прелистувач, било каде.

Популарни {име} Случаи за користење

ML експериментирање

Истражување на податоци

Модел на прототипирање

Образовни работилници

Тетратки за истражување

Кагл натпреварите

Вклучи го GPU- серверот

GPU спецификации

ГПУNVIDIA Tesla P40

VRAM24 GB GDDR5X

Јадрото на КУДА3,840

ФП3212 TFLOPS

INT847 TOPS

BW меморија346 GB/s

АрхитектураPascal (GP102)

Поминување низПЦИЕ на боите- метали

Често поставувани прашања

What is Jupyter on a GPU VPS?

Jupyter on a GPU VPS is a CUDA-accelerated deployment. Jupyter is a general GPU-accelerated workload. Make sure your software has CUDA support and that your driver / runtime versions match the workload requirements for Jupyter.

How do I set up Jupyter on a GPU VPS?

Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install jupyterlab torch && jupyter lab --ip=0.0.0.0 --allow-root. Your Jupyter environment is ready in minutes with full GPU acceleration.

How much VRAM do I need for Jupyter?

Our GPU VPS ships with 24 GB GDDR5X VRAM on the NVIDIA Tesla P40, which is sufficient for most Jupyter workloads. Multi-GPU configurations are available on request.

Is Jupyter GPU VPS billed hourly or monthly?

GPU VPS plans are billed monthly with no lock-in contracts and can be cancelled anytime. Contact us for current GPU pricing tiers.

Can I run other tools alongside Jupyter?

Yes — you have full root on the GPU VPS. Run whatever fits inside the 24 GB VRAM and the available RAM / storage budget alongside Jupyter.

Do I get full root on the Jupyter GPU VPS?

Yes. Full root SSH on every GPU VPS — install drivers, swap CUDA versions, customize the environment for Jupyter however you need.

Which CUDA version is installed for Jupyter?

GPU VPSs ship with a recent CUDA runtime and the matching NVIDIA driver pre-installed. You can pin or upgrade CUDA versions as required by your Jupyter workload.

Does my Jupyter GPU VPS persist between sessions?

Yes — your Jupyter GPU VPS is a long-running persistent server, not an ephemeral instance. Models, configs, and data stay on the SSD between sessions.

Where should I store data for my Jupyter workload?

Keep working data on the VPS SSD for fast access during Jupyter runs; back up finished artifacts (weights, generations, embeddings) off-server via snapshots or object storage for safety.

Can I scale my Jupyter GPU VPS later?

Yes — plan upgrades are instant from your control panel; the GPU itself can be swapped to a larger tier on request. Your Jupyter install carries over.

Are backups available for my GPU VPS?

Yes. Automated daily backups are an add-on; manual snapshots are free. Useful for long Jupyter training runs where you want a checkpointable server state.

Is there a money-back guarantee on the GPU VPS?

Yes — 30-day money-back guarantee on every plan including GPU. Try Jupyter on a GPU VPS risk-free.