RTX 5060 Ti 16GB as API Sidecar for AI Features

Adding AI to an existing Rails, Django, Node or PHP application rarely means rewriting the app – the cleanest pattern is an AI sidecar: a small, narrow service running on dedicated GPU hardware that your main app calls over HTTP. A RTX 5060 Ti 16GB from our dedicated GPU hosting makes an excellent sidecar for EC2, Heroku, Render, Vercel or Fly apps that need summarisation, embeddings, classification or moderation without the cost of a hyperscaler API.

Contents

• The sidecar pattern
• What the sidecar does
• Secure connection options
• Latency budget
• Integration code
• Scaling the sidecar

The Sidecar Pattern

Your main app handles CRUD, auth, payments, UI. The sidecar exposes a narrow internal API – /summarise, /embed, /classify, /transcribe. The main app calls it with HTTP POST; the sidecar returns JSON. Two benefits: the main app stays stateless and CPU-bound, and the GPU workload scales independently on dedicated hardware.

What the Sidecar Does

Most of these co-exist comfortably within 16 GB – an LLM (10 GB) plus an embedder (2 GB) plus a small classifier leaves headroom.

Secure Connection Options

For a UK-hosted sidecar serving a UK-region cloud app, WireGuard or Tailscale add almost no latency. For cross-region (US-east app -> UK sidecar), expect ~80-100 ms of network latency on top of the GPU work – consider a regional sidecar pair.

Latency Budget

Integration Code

FastAPI sidecar (Python) + a Rails client:

# sidecar.py (on the 5060 Ti)
from fastapi import FastAPI, Header, HTTPException
from openai import OpenAI
app = FastAPI()
llm = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="x")

@app.post("/summarise")
def summarise(body: dict, x_api_key: str = Header(None)):
    if x_api_key != "sk-internal": raise HTTPException(401)
    r = llm.chat.completions.create(
        model="llama-3.1-8b", max_tokens=200,
        messages=[{"role":"user","content":f"Summarise:\n{body['text']}"}]
    )
    return {"summary": r.choices[0].message.content}

# Rails client
uri = URI("https://ai-sidecar.internal/summarise")
res = Net::HTTP.post(uri, { text: content }.to_json,
  "Content-Type" => "application/json",
  "X-API-Key"    => Rails.application.credentials.sidecar_key)
JSON.parse(res.body)["summary"]

Scaling the Sidecar

• One 5060 Ti comfortably handles a few hundred requests per minute for summarisation, thousands for embeddings
• Add a second 5060 Ti on a sibling server when P95 creeps – round-robin via nginx or HAProxy
• Cache aggressively – embeddings for repeat documents, summaries keyed by content hash
• Queue long jobs (transcribe, long summaries) via Redis or RabbitMQ so synchronous requests stay snappy

See also: vLLM setup, FP8 deployment, Llama 3 8B benchmark, Docker CUDA setup, first-day checklist.