NVIDIA Tesla P40 · 24GB VRAM

Servidor de GPU vLLM

Servir modelos de linguaxe grandes co máximo rendemento usando vLLM en hardware dedicado de GPU NVIDIA. API compatíbel con OpenAI.

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

Que é vLLM nun VPS con GPU?

vLLM é un motor de servidor LLM de alto rendemento que emprega PagedAttention para unha xestión eficiente da memoria. Executar vLLM nun VPS con GPU dálle unha API LLM lista para a produción cun rendemento óptimo.

Por que vLLM en VPS.org GPU

AtenciónPaxinada

Xestión eficiente da memoria da GPU para un maior rendemento.

Lote continuo

Xestione múltiples peticións simultáneas cunha utilización óptima da GPU.

API de OpenAI

Substitución de puntos finais da API OpenAI.

Soporte de modelos

LLaMA, Mistral, Gemma, Qwen, e máis de 50 arquitecturas de modelos.

Casos de uso populares de vLLM

APIs de produción LLM
Chatbots de alto tráfico
Procesamento de texto por lotes
Servizo LLM multi- inquilino
Infraestruturas SaaS de IA
Plataformas de IA empresarial

Especificacións da GPU

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
Cores CUDA3,840
FP3212 TFLOPS
INT847 TOPS
Memoria BW346 GB/s
ArquitecturaPascal (GP102)
TransmisiónPCIe de metal desnudo

Preguntas frecuentes

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.

Listo para executar vLLM na GPU?

Despliegue un servidor dedicado de GPU NVIDIA en minutos. Sen reservas, sen chamadas de vendas.

Inicie o seu VPS
Desde $2.0/mes