--- title: "Run vLLM on a cloud GPU from $0.219/hr | PowerGPU" description: "vLLM on a fixed-price cloud GPU: one-click template, running in ~30 s, from $0.219/hr on RTX 5090. Best GPUs, volumes, per-second billing." url: https://powergpu.ai/templates/vllm last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Template · LLM serving & chat · CUDA 13 # Run vLLM on a cloud GPU, in 30 seconds Production LLM serving with PagedAttention — an OpenAI-compatible endpoint from any HF model. vLLM is the production standard for serving open LLMs: PagedAttention, continuous batching, tensor parallelism and an OpenAI-compatible API. The template exposes port 8000 with TLS, reads MODEL from an environment variable and caches weights on the instance disk or a mounted volume. From **$0.219** /hr on an interruptible RTX 5090. vLLM powergpu/vllm (CUDA 13) (ARM) (SSH) (Jupyter) Pinned image, rebuilt on upstream releases. Override env, ports and disk at deploy. ### Running in ~30 s Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this. ### Template is free You pay the GPU price only: vLLM on a RTX 5090 is $0.439/hr on-demand, $0.219/hr interruptible, billed per second. ### Volumes for state Models, datasets and outputs live on a $0.08/GB/mo volume; the instance stays disposable. ### Private by default Dedicated GPU, encrypted disk, crypto payments, no KYC, no stored IPs — Jupyter and SSH behind your own credentials. ## Best GPUs for vLLM Three price points that run this template well — Good, Better, Best. Every model in the catalogue works; these are the value picks. | Tier | GPU | VRAM | On-demand | Interruptible | Why this card | | | --- | --- | --- | --- | --- | --- | --- | | (Good) | [RTX 5090](https://powergpu.ai/gpu/rtx-5090) | 32 GB | $0.439 | $0.219 | 32 GB GDDR7: the lowest $/token for 7B–14B FP16 and 32B 4-bit models. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-5090) | | (Better) | [L40S](https://powergpu.ai/gpu/l40s) | 48 GB | $0.514 | $0.257 | 48 GB and datacenter cooling for 24/7 endpoints up to 32B. | [Deploy](https://cloud.powergpu.ai/?gpu=l40s) | | (Best) | [H100 PCIE](https://powergpu.ai/gpu/h100-pcie) | 80 GB | $1.867 | $0.933 | 80 GB for quantized 70B on one card, FP8 for maximum throughput. | [Deploy](https://cloud.powergpu.ai/?gpu=h100-pcie) | Need more VRAM? The [full catalogue](https://powergpu.ai/gpus) lists all 80 models with live availability; the [VRAM guide](https://powergpu.ai/guides/llm-vram-requirements) sizes models to cards. ## Deploy vLLM from the console, CLI or API Pick the template in the [console](https://cloud.powergpu.ai/) deploy bar, or script it: - **Console** — filter by GPU, choose *vLLM* in the template picker, set disk and env, deploy. - **CLI** — pip install powergpu, then the command on the right. [CLI reference](https://powergpu.ai/cli). - **API** — POST /v1/instances with "template": "vllm". [REST reference](https://powergpu.ai/api). - **Own image** — any OCI reference works too; we inject the NVIDIA runtime. [Template docs](https://powergpu.ai/docs/templates). *deploy — vllm* ``` $ powergpu launch --gpu rtx-5090 --template vllm \ --disk 100 --volume models:/workspace/models ✓ instance i-7a41c0e2 running (27.9s) # vLLM · RTX 5090 · $0.439/hr · per second # https://i-7a41c0e2.powergpu.ai:8888 (TLS) $ powergpu stop i-7a41c0e2 # billing ends this second ``` ## What is inside the vLLM template - **Image**: powergpu/vllm - **CUDA**: CUDA 13 - **Access**: also builds for ARM hosts · SSH shell · JupyterLab on a mapped port - **Category**: [LLM serving & chat](https://powergpu.ai/templates#llm) - **Storage**: Instance NVMe disk (sized at deploy) + optional [network volumes](https://powergpu.ai/products/volumes) - **Billing**: GPU price only, per second — no template fee, no setup fee ## Other llm serving & chat templates - [vLLM Omni on a cloud GPU](https://powergpu.ai/templates/vllm-omni) — vLLM extended for multimodal models — vision and audio inputs on the same fast server. - [SGLang on a cloud GPU](https://powergpu.ai/templates/sglang) — High-throughput serving with RadixAttention — excels at structured and agentic workloads. - [Llama.cpp on a cloud GPU](https://powergpu.ai/templates/llama-cpp) — GGUF inference with a built-in server — the lightest way to run quantized models. - [Ollama on a cloud GPU](https://powergpu.ai/templates/ollama) — Pull and run quantized models in one command, with a clean REST API. - [Open WebUI (Ollama) on a cloud GPU](https://powergpu.ai/templates/open-webui-ollama) — A full chat UI over Ollama — conversations, RAG and model management in the browser. - [Oobabooga Text Gen on a cloud GPU](https://powergpu.ai/templates/oobabooga-text-gen) — The text-generation WebUI & API — load GPTQ, EXL2 or GGUF with extensions. - [All 37 templates](https://powergpu.ai/templates) ## vLLM on a cloud GPU: FAQ Environment variables, ports and custom images are covered in the [template docs](https://powergpu.ai/docs/templates). **How much does it cost to run vLLM on a cloud GPU?** Only the GPU price — the template is free. From $0.219/hr on an interruptible RTX 5090, $0.439/hr on-demand on a RTX 5090. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month. **How long does vLLM take to start?** About 30 seconds from the deploy click: the image is pre-cached on hosts, ports are mapped and TLS is terminated for you. Restarting a stopped instance is faster, and your disk is exactly as you left it. **Can I keep my models and outputs between sessions?** Yes — attach a volume at deploy. Everything on the volume survives instance destruction and mounts on the next instance in the region in seconds, at $0.08/GB/month. The instance disk itself survives stop/start but not destroy. **Is the endpoint really OpenAI-compatible?** Yes — point any OpenAI SDK at https://.powergpu.ai:8000/v1 with the API key you set in VLLM_API_KEY. Chat, completions, embeddings and streaming all work unchanged. --- *About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API. Source: https://powergpu.ai/templates/vllm · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt