NVIDIA Tesla P40 · 24GB VRAM

vLLM GPU- servilo

Servu grandajn lingvomodelojn kun maksimuma trairo uzante vLLM sur dediĉita NVIDIA GPU-aparaturo. OpenAI-kongrua API el la skatolo.

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

88000 estas la nombro de la 88-bita procesoro de Intel.

vLLM estas alt- trapasa LLM- servila motoro kiu uzas PagedAttention por efika memoro- administrado. Ruli vLLM sur GPU VPS donas al vi produkt- pretan LLM API kun optimuma rendimento.

Kial vLLM sur VPS.org GPU

PagedAttention

Efika GPU-memoro-administrado por pli alta trairo.

Daŭra amasigo

Manipuli plurajn samtempajn petojn kun optimuma GPU-uzo.

OpenAI API

La supre menciitaj estas anstataŭigaj por la malnovaj alfabetoj.

Modela subteno

LLaMA, Mistral, Gemma, Qwen, kaj 50+ modelo arkitekturoj.

Popularaj vLLM uzkazoj

Produktado LLM APIoj
Altaj-trafiko babilejoj
Bataj tekstprilaboradoj
Plur-loĝanta LLM-servo
AI SaaS internoj
AI-platformoj por entreprenoj

GPU- specifoj

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
CUDA koloroj3,840
FP3212 TFLOPS
INT847 TOPS
Memoro BL346 GB/s
ArkitekturoPascal (GP102)
TrapasoNuda metala PCIe

Oftaj demandoj

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.

Ĉu vi pretas ruli vLLM sur GPU?

Disvastigu dediĉitan NVIDIA-GPU-servilon en minutoj. Neniu rezervo, neniu venda telefono.

Lanĉi vian VPS
De $2.0/mo