Multi-tenant LLM inference platform: –40% GPU cost
IASWITCH designed an auto-scaling LLM inference platform serving multiple models with smart routing, cutting GPU costs by 40% while meeting latency targets.
In brief
- Context: multiple models to serve for multiple teams
- Solution: auto-scaling inference + multi-model routing
- Result: GPU costs cut by 40%
- Stack: vLLM, Kubernetes, GPU
The challenge
The client needed to serve multiple LLMs to several internal teams, with fast-growing GPU costs and uneven latency. Each team deployed its own models without pooling.
The solution
We built a multi-tenant inference platform on Kubernetes with vLLM, per-request autoscaling and smart routing that directs each request to the right model (small model by default, large model when needed).
The results
GPU costs cut by 40%, latency controlled through explicit SLOs, and a single observable platform for all teams.
Frequently asked questions
How do you cut GPU costs on an LLM inference platform?
By pooling inference on a multi-tenant platform, with per-request autoscaling and multi-model routing. IASWITCH cut GPU costs by 40% this way.
What is a multi-tenant inference platform?
It is a single platform that serves multiple models to multiple teams, with isolation, pooled GPU resources and centralized observability.
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