NVIDIA Tesla P40 · 24GB VRAM

vLLM GPU Server

Serve large language models with maximum throughput using vLLM on dedicated NVIDIA GPU hardware. OpenAI-compatible API out of the box.

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

What is vLLM on a GPU VPS?

vLLM is a high-throughput LLM serving engine that uses PagedAttention for efficient memory management. Running vLLM on a GPU VPS gives you a production-ready LLM API with optimal performance.

Why vLLM on VPS.org GPU

PagedAttention

Efficient GPU memory management for higher throughput.

Continuous Batching

Handle multiple concurrent requests with optimal GPU utilization.

OpenAI API

Drop-in replacement for OpenAI API endpoints.

Model Support

LLaMA, Mistral, Gemma, Qwen, and 50+ model architectures.

Popular vLLM Use Cases

Production LLM APIs
High-traffic chatbots
Batch text processing
Multi-tenant LLM serving
AI SaaS backends
Enterprise AI platforms

GPU Specifications

GPUNVIDIA Tesla P40
VRAM24 GB GDDR5X
CUDA Cores3,840
FP3212 TFLOPS
INT847 TOPS
Memory BW346 GB/s
ArchitecturePascal (GP102)
PassthroughBare-metal PCIe

Frequently Asked Questions

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.

Ready to Run vLLM on GPU?

Deploy a dedicated NVIDIA GPU server in minutes. No reservations, no sales calls.

Launch Your VPS
From $2.0/mo