NVIDIA Tesla P40 · 24GB VRAM

TensorFlow GPU VPS

Przyspiesz obciążenia TensorFlow z dedykowanym sprzętem NVIDIA GPU. Modele pociągu, obsługiwanie przewidywań i budowanie rurociągów ML z pełnym wsparciem CUDA.

$ pip install tensorflow[and-cuda]
# Bieganie na NVIDIA Tesla P40 (24GB)
Gotowy. _

Jaka jest {nazwisko} na GPU VPS?

TensorFlow to otwarte ramy nauki maszynowej Google do budowy i wdrożenia modeli ML. Z GPU VPS, masz dedykowany sprzęt do przyspieszenia szkolenia i wyników bez udostępniania zasobów.

Dlaczego {nazwisko} na VPS.org GPU

Przyspieszony GPU

Wsparcie CUDA dla operacji TensorFlow. Do 50x szybciej niż procesor.

Keras kompatybilny

Pełna integracja Keras do budowy modeli wysokiego szczebla z interfejsem GPU.

TensorBoard

Monitor trening z TensorBoard na własnym serwerze.

TF Służba gotowa

Rozpoczynać modele do produkcji z TensorFlow Servising na GPU.

Popularne {nazwa} Przypadki użytkowania

Klasyfikacja obrazów
Wykrywanie obiektu
Przetwarzanie języka naturalnego
Systemy zaleceń
Prognoza serii czasowych
Produkcja rurociągów ML

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 TensorFlow on a GPU VPS?

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TensorFlow on a GPU VPS is a CUDA-accelerated deployment. TensorFlow is a training / fine-tuning workload. Plan for long-running jobs — snapshot your VPS regularly, and consider an external cold-storage backup for trained weights.

How do I set up TensorFlow on a GPU VPS?

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Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install tensorflow[and-cuda]. Your TensorFlow environment is ready in minutes with full GPU acceleration.

How much VRAM do I need for TensorFlow?

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Training VRAM is dominated by the optimizer state plus activations. Full fine-tuning of a 7B LLM needs ~24-48 GB; LoRA / QLoRA fits in 8-16 GB. Our Tesla P40 supports LoRA-class fine-tuning out of the box; full training of larger models requires multi-GPU.

Is TensorFlow 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 TensorFlow?

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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 TensorFlow.

Do I get full root on the TensorFlow GPU VPS?

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

Which CUDA version is installed for TensorFlow?

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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 TensorFlow workload.

Does my TensorFlow GPU VPS persist between sessions?

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Yes — your TensorFlow 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 TensorFlow workload?

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

Can I scale my TensorFlow 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 TensorFlow 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 TensorFlow 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 TensorFlow on a GPU VPS risk-free.

Gotowy do uruchomienia {nazwa} na GPU?

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

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