NVIDIA Tesla P40 · 24GB VRAM

Àwọn Ààyè-iṣẹ́ GPU

Fi àwọn àwọn àwòrán ìtàn nlà pamọ́ láti fi ìgbàyàn tójú lórí vLLM lórí àwọn ìṣàfarawékọ́ NVIDIA GPU. API OpenAI-ọ̀pọ̀lù lórí àwọn àwọn àwọn ààyè-iṣẹ́.

Fí Sáàbù GPU Hàn Àwọn Àtòjọ-ẹ̀yàn GPU

$ pip install vllm && vllm serve meta-llama/Llama-3-8B-Instruct --host 0.0.0.0
# Ń bọ́ nípa NVIDIA Tesla P40 (24GB)
Tí a tì ṣè _

Àwọn àwọn ààyè-iṣẹ́ tí a fi pamọ́

vLLM ní enginé tí a fi pamọ́ LLM tí o lò PagedAttention fún ìṣakosoí ìrànwọ́. Tí o bá rọ́ọ̀nù vLLM lórí GPU VPS, o gba ọ̀kan LLM API tí a tí fi pamọ́ lórí iṣẹ́ tí o dara jù lọ.

Kini idi ti vLLM lo lori VPS.org GPU

Àwọn Àkọlé

Ìṣakoso ìrànwọ́ GPU tí o dara fún ìṣàfarawé ìrànwọ́ giga.

Àwọn àwọn àwọn àwọn àwọn

Ṣàfikún àwọn ìtàn àìdájú àwọn ìṣàmúlò-ètò GPU tí a fẹ́.

OpenAI API

Àwọn ìṣàmúlò-ètò ìṣàfarawe-ìpamọ́ fún àwọn ààyè-iṣẹ́ API OpenAI.

Àwọn àwọn ìṣàmúlò-ètò

LLaMA, Mistral, Gemma, Qwen, atí àwọn ààtòjọ-ẹ̀yàn àwọn módèlè̀ 50+

Àwọn Ààyè Lòjútó vLLM

Àwọn API LLM Ìṣàfilọ́lẹ̀

Àwọn àkọlé àwòrán

Àwọn àwọn àkọlé

Ìṣẹ́ LLM tí a fi pamọ́

Àwọn ìsàlẹ̀-ilà SaaS AI

Àwọn àwọn ààyè-iṣẹ́ AI

Fí Sáàbù GPU Hàn

GPU Specifications

GPUNVIDIA Tesla P40

VRAM24 GB GDDR5X

Àwọn àwọ̀ CUDA3,840

FP3212 TFLOPS

INT847 TOPS

Àwọn àwọn àmì-ìwé346 GB/s

Àwọn Ìṣàmúlò-ètòPascal (GP102)

Àwọn Ìjánu-ìṣàmúlò-ètòPCIe àìdárá

Àwọn Àtòjọ-ẹ̀yàn

What is vLLM on a GPU VPS?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

Yes — 30-day money-back guarantee on every plan including GPU. Try vLLM on a GPU VPS risk-free.