NVIDIA Tesla P40 · 24GB VRAM

PyTorch GPU VPS

Tren og utnytt dyplæringsmodeller med PyTorch på dedikerte NVIDIA GPU-er. Forhåndskonfigurert CUDA-miljø med full root-tilgang.

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

Hva er {navn} på en GPU VPS?

PyTorch er det førende rammeverket for dyplæring som brukes av forskere og ingeniører over hele verden. Et GPU VPS gir deg dedikert NVIDIA- maskinvare til å trene modeller raskere og kjøre inferansier i skala.

Hvorfor {navn} på VPS.org GPU

CUDA klar

Forhåndsinnstilte NVIDIA- drivere og CUDA- verktøysett. Start opplæring med en gang.

Fullt GPU- minne

24 GB VRAM for opplæring av større modeller og større satsstørrelser.

Integrasjon med jupyter

Kjør Jupyter- notatblokker med GPU- støtte for interaktiv utvikling.

Distribuert trening

Skaler til GPU- oppsett for raskere trening av store datasett.

Populære {navn} Brukstilfelle

Opplæring i nettet for nervesystemet
Datasynsmodeller
NLP- og transformatormodeller
Generativ AI-forskning
Fininnstillingsmodell
Produksjonskonferanse

GPU-spesifikasjoner

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
CUDA-kjerner3,840
FP3212 TFLOPS
INT847 TOPS
Minne BW346 GB/s
ArkitekturPascal (GP102)
GjennomgangBare metall- PCIe

Ofte stilte spørsmål

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.

Klar til å kjøre {navn} på GPU?

Bruk en dedikert NVIDIA GPU- tjener i minutter. Ingen reservasjoner, ingen salgssamtaler.

Start din VPS
Fra $20,0/mo