Secure endpoints, manage resource limits (GPU vs CPU)

Once your assistant is deployed as an API — whether you use the Hugging Face Inference API, FastAPI, or TorchServe — you must think about security, resource usage, and cost control.

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✅ 1️⃣ Why Secure Your Endpoints?

When you expose a model as an HTTP endpoint:

• Anyone with the URL can send requests.

• Without limits, people could abuse it, run huge prompts, or rack up GPU bills.

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🔑 Basic Security Measures

✔️ API Keys / Tokens

Require a secret key in the request header or query:

POST /generate
Authorization: Bearer YOUR_SECRET_KEY

Example in FastAPI:

from fastapi import Header, HTTPException

API_KEY = "YOUR_SECRET_KEY"

@app.post("/generate")
def generate(query: Query, authorization: str = Header(None)):
    if authorization != f"Bearer {API_KEY}":
        raise HTTPException(status_code=401, detail="Unauthorized")
    # Do generation...

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✔️ CORS & Origins

For web clients, use CORS rules to block unauthorized domains:

from fastapi.middleware.cors import CORSMiddleware

app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://yourfrontend.com"],
    allow_methods=["*"],
    allow_headers=["*"],
)

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✔️ Rate Limiting

Prevent overload or misuse:

• Add rate limiting with a library like slowapi or fastapi-limiter.

• Or deploy behind an API gateway (Nginx, Traefik) with throttling.

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✔️ Input Validation

Reject requests with huge prompts or unsafe instructions:

if len(query.prompt) > 500:
    raise HTTPException(status_code=400, detail="Prompt too long")

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✅ 2️⃣ Manage GPU vs CPU Usage

Running LLMs on GPUs is faster, but GPU time is expensive. You might want to:

• Run heavy tasks on GPU, light tasks on CPU.

• Limit which models need a GPU.

• Use quantized models to fit on small hardware.

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⚙️ Check Available Hardware

import torch

if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

When you build your pipeline:

generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device=0 if torch.cuda.is_available() else -1
)

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✅ Tips for Resource Management

✔️ Use FP16 or 8-bit quantization for big models:

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16
)

✔️ If running multiple models, spin up separate containers with pinned GPU/CPU quotas. ✔️ Use accelerate configs to shard big models across multiple GPUs. ✔️ Watch VRAM usage — avoid loading multiple big models on the same GPU if you can’t fit them.

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✅ 3️⃣ Monitor & Scale

• Track logs and usage: uvicorn + FastAPI output, or plug into Prometheus/Grafana.

• Add autoscaling (Kubernetes, cloud auto-scaling groups) for traffic spikes.

• Watch billing dashboards if using pay-as-you-go GPUs.

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🗝️ Key Takeaway

✅ A powerful LLM server is only good if it’s:

• 🔒 Secure from misuse

• ⚙️ Resource-optimized (GPU/CPU balance)

• 💸 Cost-controlled

Lock it down, keep it efficient, and you’re ready for real-world users.

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➡️ Next: Learn how to evaluate your assistant’s answers, monitor quality, and collect user feedback to keep improving!