We’ve been working on a GPU-first inference platform focused on predictable latency and cost control for production AI workloads.
Some of the engineering problems we ran into:
– GPU cold starts and queue scheduling
– Multi-tenant isolation without wasting VRAM
– Model loading vs container loading tradeoffs
– Batch vs real-time inference routing
– Handling burst workloads without long-term GPU reservation
– Cost predictability vs autoscaling behavior
We wrote up the architecture decisions, what failed, and what worked.
Happy to answer technical questions – especially around GPU scheduling, inference optimization, and workload isolation.