{"slug": "llmpanel-deploy-vllm-to-runpod-or-vast-ai-without-kubernetes", "title": "LLMPanel Deploy vLLM to RunPod or Vast.ai Without Kubernetes", "summary": "LLMPanel has launched an open-source platform that deploys large language models on any GPU cloud, including RunPod and Vast.ai, without requiring Kubernetes. The tool provisions containers, exposes OpenAI-compatible endpoints, and streams GPU metrics to a single dashboard, supporting models like meta-llama/Llama-3.3-70B-Instruct on 2× H100 80GB at $4.38/hr. It integrates with vLLM and Ollama and offers features such as scoped API keys, real-time monitoring, and multi-provider fleet management.", "body_md": "# Deploy LLMs on any GPU, anywhere\n\nPick a model, pick a GPU, hit deploy. LLMPanel provisions the container, exposes an OpenAI-compatible endpoint, and streams every GPU metric back to one dashboard — on your own hardware or any cloud.\n\nMIT licensed · no GPU lock-in · works with vLLM and Ollama\n\n- Model\n- meta-llama/Llama-3.3-70B-Instruct\n- Provider\n- RunPod\n- GPU\n- 2× H100 80GB\n- Cost\n- $4.38/hr\n- Endpoint\n- llama-70b.gw.llmpanel.io/v1\n\n- RunPod\n- Vast.ai\n- Lambda\n- CoreWeave\n- Nebius\n- Crusoe\n- Hyperstack\n- FluidStack\n- Paperspace\n- TensorDock\n- RunPod\n- Vast.ai\n- Lambda\n- CoreWeave\n- Nebius\n- Crusoe\n- Hyperstack\n- FluidStack\n- Paperspace\n- TensorDock\n\n## Running your own LLMs shouldn't need a platform team\n\nSelf-hosting inference today means stitching together clouds, containers, keys and dashboards. Every piece works — none of it works together.\n\n### Scattered tooling\n\n- A different dashboard for every GPU cloud\n- Hand-written docker run commands nobody remembers\n- One API key shared by every app and teammate\n- nvidia-smi in a tmux pane is the monitoring\n- No idea what a model actually costs per hour\n\n### One control plane for inference\n\n- Docker hosts, RunPod and Vast.ai in one fleet view\n- Pick a model, pick a GPU, deploy — vLLM or Ollama\n- Scoped keys with rpm / tpm / daily caps and revocation\n- Per-GPU utilisation, VRAM and temperature, live\n- Cost per hour and uptime on every deployment\n\n### One-click deployments\n\nPick a model from the catalog, choose a GPU, hit deploy. The container, weights and endpoint are handled in minutes.\n\n### OpenAI-compatible gateway\n\nEvery deployment sits behind one API with managed keys, rate limits and routing. Swap models without touching your app.\n\n### Real-time GPU monitoring\n\nUtilisation, VRAM, temperature and tokens per second streamed live to your dashboard for every card.\n\n### Multi-provider fleet\n\nRun on your own Docker hosts or burst to RunPod and Vast.ai with your API keys — the same workflow either way.\n\n### One endpoint, scoped keys, no surprises\n\nEvery deployment sits behind one OpenAI-compatible base URL. Hand each app or environment its own key with rpm, tpm and daily caps — and revoke it in one click when it leaks.\n\n[Learn more](/api-reference)\n\n### See what every GPU is doing, right now\n\nUtilisation, VRAM, temperature and power draw for every card across Docker hosts and cloud providers, plus container CPU and RAM — live and historical, no exporters to wire up.\n\n[Learn more](/features)\n\n### Drill into any request\n\nStatus, latency, prompt and completion token split, and which key made the call. Filter by status, model, key or time range and open the full payload inline.\n\n[Learn more](/features)\n\n### Ground a model in your own docs\n\nAttach an agent to any deployment with its own system prompt and knowledge base. Upload files, get chunking and embeddings for free, and test it in the built-in chat before wiring it to Telegram or Discord.\n\n[Learn more](/integrations)\n\nWhich GPU do I need for Llama 3.3 70B?\n\nLlama 3.3 70B in FP8 needs roughly 70 GB of VRAM for weights, so a single H100 80GB works for short contexts. For 32k context, deploy on 2× H100 with tensor parallelism — the catalog preset does this for you.\n\nsource · docs/sizing-guide.md## Everything between a checkpoint and production\n\nLLMPanel covers the full life of a model in production — so your team stops stitching tools together.\n\n### Deploy\n\nShip any Hugging Face model on vLLM or Ollama to a Docker host or a cloud GPU in one flow.\n\n### Route\n\nOne OpenAI-compatible base URL in front of every deployment. Point your SDK at it and go.\n\n### Monitor\n\nPer-GPU metrics, request logs and usage analytics — live and historical, no exporters to wire up.\n\n### Scale\n\nAdd a second host or burst to RunPod when demand spikes; the gateway keeps the same URL.\n\n### Secure\n\nScoped API keys with rpm, tpm and daily caps, expiry, per-key usage and one-click revocation.\n\n### Back up\n\nSnapshot panel data and config on a schedule, and restore from any point when you need to.\n\n## Live in an afternoon\n\nNo Kubernetes, no YAML. Three steps from a bare server to your first OpenAI-compatible endpoint.\n\n### Install the panel\n\nOne command on any Linux box with Docker. It brings up the API, the dashboard and the gateway, and prints the login URL.\n\n```\ncurl -fsSL https://raw.githubusercontent.com/zf0x00/openpanel-oss/master/install.sh | bash\n```\n\n### Connect your GPUs\n\nAdd a local Docker host, or paste a RunPod / Vast.ai API key. Every card shows up in the fleet view with live metrics.\n\n### Deploy and call it\n\nPick a model, pick a GPU, deploy. Point any OpenAI SDK at the gateway URL with a scoped key — nothing else changes.\n\n``` python\nfrom openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"https://gw.llmpanel.io/v1\",\n    api_key=\"lp_live_…\",\n)\nclient.chat.completions.create(\n    model=\"llama-3.3-70b\",\n    messages=[{\"role\": \"user\", \"content\": \"hi\"}],\n)\n```\n\n## Works with your stack\n\nBring the GPUs, engines and SDKs you already use. LLMPanel sits in the middle without asking you to migrate anything.\n\n- DockerLocal hosts\n- RunPodCloud GPUs\n- Vast.aiCloud GPUs\n- LambdaCloud GPUs\n- HyperstackCloud GPUs\n- vLLMInference engine\n- OllamaInference engine\n- Hugging FaceModel catalog\n- OpenAI SDKAny language\n- TelegramChannel\n- DiscordChannel\n- WebhooksEvents & alerts\n\n## Self-host free, or let us run it\n\nLLMPanel is open source and free to self-host, forever. Pick a cloud plan when you'd rather not babysit the panel itself.\n\nFree forever. Run the full platform on your own hardware or cloud.\n\nOpen source, no strings attached\n\n[Deploy yourself](https://github.com/zf0x00/openpanel-oss)\n\n- Full platform, every feature included\n- Unlimited deployments & models\n- Bring your own GPUs or cloud keys\n- OpenAI-compatible gateway & API keys\n- Community support on GitHub\n\nFor one developer running a handful of models. We host the panel.\n\nFirst 50 customers: $9/mo, forever\n\n[Start with Solo](https://app.llmpanel.io/login)\n\n- 4 deployments included\n- $8/mo per extra deployment\n- Single seat\n- Hosted dashboard — no panel to babysit\n- OpenAI-compatible gateway & API keys\n\nFor teams sharing a GPU fleet across one workspace.\n\nExtra deployments just $5/mo each\n\n[Start with Team](https://app.llmpanel.io/login)\n\n- Everything in Solo\n- 10 deployments included\n- $5/mo per extra deployment\n- Unlimited team members\n- Shared workspace with roles & invites\n\nEvery plan includes the OpenAI-compatible gateway and per-GPU metrics.\n\n## Put your models on your own GPUs today\n\nInstall in five minutes, deploy in one click, and call it from any OpenAI SDK. Free to self-host, forever.", "url": "https://wpnews.pro/news/llmpanel-deploy-vllm-to-runpod-or-vast-ai-without-kubernetes", "canonical_source": "https://llmpanel.io", "published_at": "2026-08-25 05:40:47+00:00", "updated_at": "2026-08-25 06:12:58.848525+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-infrastructure", "mlops", "developer-tools"], "entities": ["LLMPanel", "RunPod", "Vast.ai", "vLLM", "Ollama", "meta-llama/Llama-3.3-70B-Instruct", "H100 80GB"], "alternates": {"html": "https://wpnews.pro/news/llmpanel-deploy-vllm-to-runpod-or-vast-ai-without-kubernetes", "markdown": "https://wpnews.pro/news/llmpanel-deploy-vllm-to-runpod-or-vast-ai-without-kubernetes.md", "text": "https://wpnews.pro/news/llmpanel-deploy-vllm-to-runpod-or-vast-ai-without-kubernetes.txt", "jsonld": "https://wpnews.pro/news/llmpanel-deploy-vllm-to-runpod-or-vast-ai-without-kubernetes.jsonld"}}