Ever open your cloud invoice and really feel such as you’ve been punched?
Your ML mannequin’s a celebrity.
It’s serving killer suggestions or zapping spam in real-time.
However these GPU prices?
They’re climbing quicker than a meme on X.
Multi-model serving is your lifeline.
It’s like packing your fashions right into a budget-friendly minivan.
You save huge, preserve latency tight, and possibly dodge that awkward price range assembly.
Let’s break down the right way to make it work.
Think about you’re operating ML for an e-commerce platform.
Your fashions deal with product suggestions, advert focusing on, and stock forecasting.
Every has its personal rhythm:
- Suggestions: Regular, with Black Friday spikes.
- Adverts: Nuts throughout night hours.
- Stock: Low, random bursts.
Single-model serving?
Every mannequin will get its personal VM.
That’s three servers, burning money, even when idle.
On Google Cloud, a big VM (32 vCPUs, 128GB RAM) prices a bit.
Add an NVIDIA A100 GPU (12 vCPUs…
