NVIDIA Tesla P40 · 24GB VRAM

PyTorch GPU VPS

Vježbati i raspoređivati modele dubokog učenja s PyTorch na posvećenom NVIDIA GPUs. Unaprijed konfigurirano CUDA okruženje s punim root pristupom.

$ pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
# Trčanje na NVIDIA Tesla P40 (24GB)
Spreman. _

Što je {ime} na GPU VPS?

PyTorch je vodeći okvir dubokog učenja koji koriste istraživači i inženjeri diljem svijeta. GPU VPS vam daje posvećen NVIDIA hardver za treniranje modela brže i pokrenuti zaključak na skali.

Zašto {ime} na VPS.org GPU

CUDA Spremna

Unaprijed postavljeni NVIDIA vozači i CUDA toolkit. Počnite trenirati odmah.

Potpuna GPU memorija

24GB VRAM za obuku većih modela i većih veličina serije.

Jupyter integracija

Pokrenite Jupyter bilješke uz GPU podršku za interaktivni razvoj.

Raspoređeni trening

Skaliraj na više GPU postavki za brži trening na velikim skupovima podataka.

Popularne {ime} Slučaji korištenja

Osposobljavanje za neuralne mreže
Modeli računalnog vida
NLP & transformator modeli
Generativno istraživanje AI
Uzorak finog uklapanja
Zaključak o proizvodnji

GPU specifikacije

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
CUDA jezgre3,840
FP3212 TFLOPS
INT847 TOPS
Memorija BW346 GB/s
ArhitekturaPascal (GP102)
ProlazPCIe od borovog metala

Česta pitanja

What is PyTorch on a GPU VPS?

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PyTorch on a GPU VPS is a CUDA-accelerated deployment. PyTorch 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 PyTorch on a GPU VPS?

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Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124. Your PyTorch environment is ready in minutes with full GPU acceleration.

How much VRAM do I need for PyTorch?

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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 PyTorch 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 PyTorch?

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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 PyTorch.

Do I get full root on the PyTorch GPU VPS?

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

Which CUDA version is installed for PyTorch?

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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 PyTorch workload.

Does my PyTorch GPU VPS persist between sessions?

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Yes — your PyTorch 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 PyTorch workload?

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

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

Spreman za pokretanje {ime} na GPU?

Pokrenite posvećeni NVIDIA GPU poslužitelj u minutama. Bez rezervacije, bez prodajnih poziva.

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Od $2.0/mo