NVIDIA Tesla P40 · 24GB VRAM

ວິທະຍາສາດຂໍ້ມູນ GPU VPS

ຂະບວນການຂໍ້ມູນທີ່ໃຫຍ່ຫຼວງ 10-100x ໄວຂຶ້ນດ້ວຍເຄື່ອງມືວິທະຍາສາດຂໍ້ມູນທີ່ເພີ່ມຄວາມໄວ GPU. RAPIDS, Jupyter, ແລະ PyData ເຕັມທີ່ໃສ່ຮາດແວ NVIDIA.

ຕິດຕັ້ງម៉ាស៊ីនបម្រើ GPU ຕົວເລືອກ GPU ທັງໝົດ

$ pip install cudf-cu12 cuml-cu12 jupyterlab && jupyter lab --ip=0.0.0.0
# ແລ່ນຢູ່ເທິງ NVIDIA Tesla P40 (24GB)
ພ້ອມແລ້ວ _

ຫຍັງຄື {ຊື່} ຢູ່ໃນ GPU VPS?

ວິທະຍາສາດຂໍ້ມູນທີ່ເພີ່ມຄວາມໄວໂດຍ GPU ໃຊ້ NVIDIA RAPIDS ເພື່ອປະຕິບັດ pandas, scikit-learn ແລະເຄື່ອງມືຂໍ້ມູນອື່ນໆໂດຍກົງໃນ GPU. ຂະບວນການເກັບຂໍ້ມູນທີ່ໃຊ້ເວລາຫຼາຍຊົ່ວໂມງໃນ CPU ໃນນາທີ.

ເຮັດຫຍັງ {ຊື່} ຢູ່ໃນ VPS.org GPU

ຊຸດ RAPIDS

cuDF (GPU pandas), cuML (GPU scikit-ຮຽນຮູ້), cuGraph (GPU NetworkX).

ພ້ອມໃຊ້ແລ້ວ

JupyterLab ຖືກຕັ້ງຄ່າລ່ວງໜ້າດ້ວຍການສະໜັບສະໜູນ GPU.

ກຸ່ມຂໍ້ມູນໃຫຍ່

24GB GPU ຄວາມ ຈຳ ສຳ ລັບການປຸງແຕ່ງຂໍ້ມູນໃນຄວາມ ຈຳ.

បង្ហាញជារូបភាព

ການສະແດງພາບທີ່ໄວຂຶ້ນດ້ວຍ GPU ລວມມີ cuXfilter ແລະ Plotly.

ຕົວຢ່າງການໃຊ້ທີ່ນິຍົມ {ຊື່}

ການວິເຄາະຊັບພະຍາກອນຂໍ້ມູນໃຫຍ່

ເຕັກນິກຄຸນລັກສະນະ

ການເພີ່ມຄວາມໄວ ETL

ແບບແບບສະຖິຕິ

ການວິເຄາະກຣາຟ

ການວິເຄາະພື້ນທີ່

ຕິດຕັ້ງម៉ាស៊ីនបម្រើ GPU

ຄຸນສົມບັດຂອງ GPU

GPUNVIDIA Tesla P40

VRAM24 GB GDDR5X

ពណ៌ CUDA3,840

FP32 12 TFLOPS

INT847 TOPS

ຄວາມຈຳສີຂາວ346 GB/s

ສະຖາປັດຕະຍະກໍາPascal (GP102)

ຜ່ານPCIe ແບບບໍ່ມີໂລຫະ

ຄໍາຖາມທີ່ຖາມເລື້ອຍໆ

What is Data Science on a GPU VPS?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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