NVIDIA Tesla P40 · 24GB VRAM

vLLM GPU 서버

전용 NVIDIA GPU 하드웨어에서 vLLM을 사용하여 최대 처리량으로 대규모 언어 모델을 제공합니다.

$ pip install vllm && vllm serve meta-llama/Llama-3-8B-Instruct --host 0.0.0.0
# NVIDIA Tesla P40 (24GB)에서 실행 중
준비됐어요 _

GPU VPS에서 vLLM는 무엇입니까?

vLLM은 PagedAttention을 사용하여 효율적인 메모리 관리를 제공하는 고처리량 LLM 서빙 엔진입니다. GPU VPS에서 vLLM을 실행하면 최적의 성능을 갖춘 프리미엄 LLM API를 얻을 수 있습니다.

VPS.org GPU에서 vLLM을 사용하는 이유

페이지 주의

효율적인 GPU 메모리 관리로 처리량 향상.

연속 배치

최적의 GPU 활용도로 여러 개의 동시 요청을 처리합니다.

오픈AI API

OpenAI API 엔드포인트를 위한 드롭인 대체.

모델 지원

LLaMA, Mistral, Gemma, Qwen, 50+ 모델 아키텍처.

인기 있는 vLLM 사용 사례

생산 LLM API
트래픽이 많은 채팅봇
일괄 텍스트 처리
다중 테넌트 LLM 제공
AI SaaS 백엔드
엔터프라이즈 AI 플랫폼

GPU 사양

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
CUDA 색상3,840
FP32 플랫폼12 TFLOPS
INT847 TOPS
메모리 흑백346 GB/s
아키텍처Pascal (GP102)
통과베어 메탈 PCIe

자주 묻는 질문

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.

GPU에서 vLLM를 실행할 준비가 되셨습니까?

전용 NVIDIA GPU 서버를 몇 분 안에 배포하세요. 예약도, 영업 전화도 필요 없습니다.

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