NVIDIA Tesla P40 · 24GB VRAM

Servidor de la GPU vLLM

Serve grans models de llenguatge amb màxima rendiment utilitzant vLLM en el maquinari NVIDIA dedicat a la GPU. OpenAI- compatible amb l' API de la caixa.

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

Què és el vLLM en un vicepresident de la GPU?

vLLM és un motor que fa servir LLLLLLLLM que utilitza la intenció de realitzar memòria eficient. S' està executant vLM en un director de la GPU, us dóna una API de producció ja que ja està a punt amb rendiment òptim.

Why vLLM on VPS.org GPU

Attenció de pàgina

Gestor de memòria de la GPU eficient per a un alt rendiment.

per lots continuos

Manega múltiples peticions concurrents amb ús òptima de la GPU.

OpenAI API

Reemplaça els punts finals de l' API OpenAI.

Implementació del model

LLaMA, Mistal, Gemma, Qwen, i 50 arquitectures de model+.

Casos d' ús famosos vLLM

Producció les API LLLM
Carrers d' alta conversa
Processament de text per lots
Funcionament multi-tenant LLLLLM
Dorsals IA SaaS
plataformes de IA Enterprise

Especificacions de la GPU

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
CUDA Cores3,840
FP3212 TFLOPS
INT847 TOPS
Memòria BW346 GB/s
ArquitecturaPascal (GP102)
PassatBare- metàl· lic PCIe

Preguntes més freqüents

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.

A punt per executar vLLM a la GPU?

Deploy un servidor de la GPU de la NVIDIA dedicat en minuts. No hi ha reserves, ni trucades de vendes.

Inicieu els vostres directors.
Des de $2. 0/mo