{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "patchy631-time-to-first-token--serve-openai-compatible-llm-on-rented-gpu",
 "repo": "patchy631/time-to-first-token",
 "repo_url": "https://github.com/patchy631/time-to-first-token",
 "repo_slug": "patchy631-time-to-first-token",
 "title": "Serve a 7-8B model behind an OpenAI-compatible endpoint",
 "page_title": "How to serve a 7-8B model behind an OpenAI-compatible endpoint with Time to First Token",
 "summary": "Deploy vLLM on one rented 24GB GPU and call it with OpenAI-style requests.",
 "who": "Backend engineer who needs a self-hosted LLM endpoint and has no prior serving experience.",
 "problem": "Running an LLM in production starts with GPU setup, serving, and API compatibility, and those steps block any real work.",
 "outcome": "A working endpoint serving Llama-3.1-8B-Instruct or Qwen2.5-7B that existing OpenAI-style client code can call.",
 "steps": [
  "Rent one 24GB GPU by the half hour, for example on RunPod or Modal.",
  "`pip install vllm`",
  "Start the server with `vllm serve meta-llama/Llama-3.1-8B-Instruct`, using vLLM's OpenAI-compatible server as the README describes. (inferred, check the README)",
  "Send one chat request to the endpoint from an OpenAI-compatible client and confirm a response comes back.",
  "Save the launch command, since the roadmap reuses it for the following ten weeks."
 ],
 "agent_prompt": "Using the patchy631/time-to-first-token repo as the guide, set up vLLM on <GPU host> serving Qwen2.5-7B behind an OpenAI-compatible API. Expected output: a chat completion response from the endpoint. Verify that the response contains a non-empty assistant message.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "The README does not give the exact launch command, so confirm the vLLM flags against the version you install.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "html",
 "requirements": [
  "Python and command-line comfort",
  "One rented GPU with about 24GB of memory for a 7-8B model, billed by the half hour",
  "Docker for the Prometheus and Grafana stack",
  "An H100 for the 1000-concurrent load test in week 5"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/patchy631-time-to-first-token/serve-openai-compatible-llm-on-rented-gpu/",
 "md": "https://dedupedai.com/use-cases/patchy631-time-to-first-token/serve-openai-compatible-llm-on-rented-gpu/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/patchy631-time-to-first-token--serve-openai-compatible-llm-on-rented-gpu.json",
 "api_version": "v1"
}