I will deploy open source llm on runpod or your GPU server with fastapi


About this gig
You have a GPU server (RunPod, Vast.ai, AWS, or your own) I'll get an open-source LLM running on it, production-ready, in days.
What you get:
- The RIGHT model for your hardware: Llama 3.1, Qwen 2.5, or Mistral, quantized (4-bit AWQ/GPTQ/GGUF) to fit your VRAM without wrecking answer quality
- Fast inference: vLLM or Ollama, configured for your latency and throughput needs
- Streaming FastAPI endpoint (SSE or WebSocket) your app can call like the OpenAI API, but yours
- Restartable with a single script + README with every command rebuild the server from scratch in minutes
- Your data never leaves your infrastructure. Zero per-token API costs, ever.
Why me: I've deployed quantized open-source LLMs on RunPod GPU infrastructure with streaming FastAPI endpoints including SLM training and deployment pipelines. 8+ years in software & data engineering. Python, vLLM, Ollama, Docker, AWS.
Before ordering, message me with your GPU spec (or your use case if you haven't rented yet I'll recommend the cheapest GPU that fits). It takes 2 minutes and guarantees the right package.
Get to know Inferon Labs
AI and LLM Deployment Engineer, RAG Chatbots, FastAPI Backends
- FromIndia
- Member sinceJun 2026
- Avg. response time1 hour
Languages
English
FAQ
Which GPU do I need?
Depends on model size: 7–8B models run well on 16–24GB (RTX 4090/A5000), 14B+ wants 24–48GB. Message me your use case and I'll recommend the cheapest option that fits.
I haven't rented a server yet — can you help me choose?
Yes, included free. I'll point you to the best price/performance on RunPod or alternatives before you spend anything.
Will this cost me monthly API fees?
No. Open-source models on your own GPU = you pay only the server rental. No per-token charges.
Can you also connect my documents (RAG)?
Yes — that's the Premium package, or see my dedicated RAG chatbot gig.
Do you need access to my server?
SSH or the RunPod console, your choice. Everything I install is documented in the README, and you can revoke access the moment we're done.
