NVIDIA Tesla P40 · 24GB VRAM

Jupyter GPU ноутбук сервери

Jupyter ноутбуктарын NVIDIA GPU колдоосу менен иштетүү. Интерактивдүү маалыматтар илими жана ML иштеп чыгуу CUDA тездетүү менен.

Серверди орнотуу Бардык GPU параметрлери

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

GPU VPS'те Jupyter деген эмне?

GPU VPS'теги Jupyter ноутбуктары сизге NVIDIA GPU аппаратурасы менен интерактивдүү иштеп чыгуу чөйрөсүн берет. Маалымат илимине, ML эксперименттерине жана моделдин прототиптерине идеалдуу.

Jupyter VPS.org GPU'да эмне үчүн

Ноутбуктардагы GPU

CUDA GPU-ларга ноутбуктан түз эле кирүү. Интерактивдүү түрдө моделдерди даярдоо.

JupyterLab

Терминал, файлдарды кароо жана кеңейтүүлөрү бар толук JupyterLab IDE

Алдын ала орнотулган китепканалар

PyTorch, TensorFlow, scikit-learn, pandas жана башкалар колдонууга даяр.

Жакынкы кирүү

Бардык браузерлерден жана бардык жерден өз ноутбуктарыңызга кирүү.

Jupyter популярные варианты использования

ML эксперименти

Маалыматтарды изилдөө

Прототипти түзүү

Билим берүү

Изилдөө ноутбуктары

Kaggle сынактары

Серверди орнотуу

Графикалык процессордун мүнөздөмөсү

Графикалык процессорNVIDIA Tesla P40

ВРМ24 GB GDDR5X

CUDA түстөрү3,840

FP3212 TFLOPS

INT847 TOPS

Жад346 GB/s

АрхитектураPascal (GP102)

Кеңири өтмөкБар-метал PCIe

Кайсы убакта суроолор берилет

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.