NVIDIA Tesla P40 · 24GB VRAM

Nauka danych GPU VPS

Przetwarzanie masywnych zbiorów danych 10-100x szybciej z narzędziami nauczania danych przyspieszonym GPU. RAPIDS, Jupyter i pełny stos PyData na sprzętu NVIDIA.

$ pip install cudf-cu12 cuml-cu12 jupyterlab && jupyter lab --ip=0.0.0.0
# Bieganie na NVIDIA Tesla P40 (24GB)
Gotowy. _

Jaka jest {nazwisko} na GPU VPS?

Nauka o przyspieszonych danych GPU wykorzystuje NVIDIA RAPIDS do uruchamiania pand, scikit-learner i innych narzędzi danych bezpośrednio na GPU. Zbiór danych procesowych, który zajmowałby godziny na procesorze w minutach.

Dlaczego {nazwisko} na VPS.org GPU

Suite RAPIDS

CuDF (GPU pandy), cuML (GPU scikit-learner), cuGraph (GPU NetworkX).

Jupyter gotowy

Wstępnie dostosowany JupyterLab z obsługą GPU.

Duże zestawy danych

24GB pamięci GPU do przetwarzania danych w pamięci.

Wizualizacja

Wizualizacja przyspieszona GPU z programem cuXfilter i Plotly.

Popularne {nazwa} Przypadki użytkowania

Duża analiza zbioru danych
Inżynieria funkcji
Przyspieszenie ETL
Modelowanie statystyczne
Analiza wykresu
Analiza geoprostorowa

Specyfikacje GPU

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
Rdzeń CUDA3,840
FP3212 TFLOPS
INT847 TOPS
Pamięć BW346 GB/s
ArchitekturaPascal (GP102)
PrzejścieBare-metal PCIe

Często zadawane pytania

What is Data Science on a GPU VPS?

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

How do I set up Data Science on a GPU VPS?

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Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install cudf-cu12 cuml-cu12 jupyterlab && jupyter lab --ip=0.0.0.0. Your Data Science environment is ready in minutes with full GPU acceleration.

How much VRAM do I need for Data Science?

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

Is Data Science GPU VPS billed hourly or monthly?

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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 Data Science?

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

Do I get full root on the Data Science GPU VPS?

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

Which CUDA version is installed for Data Science?

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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 Data Science workload.

Does my Data Science GPU VPS persist between sessions?

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Yes — your Data Science 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 Data Science workload?

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

Can I scale my Data Science GPU VPS later?

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

Are backups available for my GPU VPS?

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

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

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

Gotowy do uruchomienia {nazwa} na GPU?

Wysłać dedykowany serwer NVIDIA GPU w minutach. Żadnych rezerwacji, żadnych połączeń sprzedaży.

Uruchom VPS
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