NVIDIA Tesla P40 · 24GB VRAM

Server GPU vLLM

Servire grandi modelli di lingua con il massimo throughput utilizzando vLLM su hardware dedicato NVIDIA GPU. API OpenAI-compatibile fuori dalla scatola.

$ pip install vllm && vllm serve meta-llama/Llama-3-8B-Instruct --host 0.0.0.0
# Esecuzione su NVIDIA Tesla P40 (24GB)
Pronti. _

Che cos'è {nome} su un VPS GPU?

vLLM è un motore di servizio LLM ad alta velocità che utilizza PagedAttention per una gestione efficiente della memoria. L'esecuzione di vLLM su un VPS GPU offre una API LLM pronta alla produzione con prestazioni ottimali.

Perché {nome} su VPS.org GPU

Attenzione alla pagina

Gestione efficiente della memoria GPU per un maggiore throughput.

Lotto continuo

Gestisci più richieste concorrenti con un utilizzo ottimale della GPU.

API OpenAI

Sostituzione del drop-in per gli endpoint API OpenAI.

Supporto del modello

Architetture di modelli LLaMA, Mistral, Gemma, Qwen e 50+.

Casi di utilizzo popolari {nome}

API LLM di produzione
Chatbot ad alto traffico
Elaborazione del testo del lotto
Multi-tenant LLM che serve
AI SaaS backends
Piattaforme IA per le imprese

Specifiche GPU

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
Core CUDA3,840
32° PQ12 TFLOPS
INT847 TOPS
Memoria BW346 GB/s
ArchitetturaPascal (GP102)
PassaggioPCI Bare-metal

Domande frequenti

What is vLLM on a GPU VPS?

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vLLM on a GPU VPS is a CUDA-accelerated deployment. vLLM is primarily an LLM-inference / chat workload. You will want fast random-access reads from disk to memory and enough VRAM for the model plus context window.

How do I set up vLLM on a GPU VPS?

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Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install vllm && vllm serve meta-llama/Llama-3-8B-Instruct --host 0.0.0.0. Your vLLM environment is ready in minutes with full GPU acceleration.

How much VRAM do I need for vLLM?

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LLM inference VRAM scales with model parameters. A 7B model needs ~5-8 GB VRAM, 13B ~10-14 GB, 70B requires multi-GPU or quantization. Our 24 GB Tesla P40 comfortably runs 7B-13B models at full precision and 30B-class models with INT8 quantization.

Is vLLM 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 vLLM?

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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 vLLM.

Do I get full root on the vLLM GPU VPS?

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

Which CUDA version is installed for vLLM?

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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 vLLM workload.

Does my vLLM GPU VPS persist between sessions?

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Yes — your vLLM 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 vLLM workload?

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

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

Pronto per eseguire {nome} sulla GPU?

Distribuisci un server dedicato NVIDIA GPU in pochi minuti. Nessuna prenotazione, nessuna chiamata di vendita.

Lancia il tuo VPS
Da $ 2,0/mo