Self-Hosting LLMs with FastAPI

Why Self-Host?

Self-hosting LLMs gives you complete control over your AI infrastructure:

• Privacy: Data never leaves your servers
• Cost: No per-token charges after initial setup
• Customization: Fine-tune for your specific use case

Hardware Requirements

For Llama2-7B:

• 16GB+ RAM
• NVIDIA GPU with 8GB+ VRAM (or CPU with patience)
• 50GB disk space

Setting Up the Environment

python -m venv llm-env
source llm-env/bin/activate
pip install torch transformers fastapi uvicorn

Loading the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

Building the FastAPI Server

from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class ChatRequest(BaseModel):
    message: str
    max_tokens: int = 256
@app.post("/chat")
async def chat(request: ChatRequest):
    inputs = tokenizer(request.message, return_tensors="pt")
    outputs = model.generate(inputs, max_new_tokens=request.max_tokens)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"response": response}

Production Deployment

Use Gunicorn with Uvicorn workers:

gunicorn main:app -w 2 -k uvicorn.workers.UvicornWorker

Conclusion

You now have a private, scalable LLM API. Consider adding rate limiting, authentication, and monitoring for production use.