- Tuned the serving engine to match request size distribution and concurrency behavior (vLLM tuning is a good reference point for this type of work)
- Improved device-aware placement using Kubernetes device plugin patterns so specialized hardware is advertised cleanly to the scheduler
- Reduced CPU bounce buffering behavior in the data path where feasible
The Kubernetes device plugin framework is the simple building block behind making “specialized resources” schedulable at scale.
Outcome
- More linear scaling as GPUs were added
- Stabilized TPOT p99 because fewer requests were blocked behind slow neighbors
- Reduced CPU overhead, freeing headroom for networking and observability
Open source that fits these patterns
You can implement most of these improvements using open-source components:
- Observability: Prometheus, Grafana, OpenTelemetry and eBPF-based tooling to see flow-level latency and fan-out.
- Caching: Redis for hot key/value caching; local NVMe caches for hot artifacts.
- Serving: vLLM for configurable batching and memory behavior under load.
- Scheduling: Kubernetes device plugins and resource-aware node pools for GPU and NIC locality. (Kubernetes device plugins:)
- Storage: Ceph is a common open-source option for software-defined block, file and object patterns. IBM also calls out IBM AI Storage Ceph as an open source, software-defined approach aligned to these needs.
Limitations and tradeoffs
Every performance win has an operational cost. These are the tradeoffs I plan for.
- Caching improves consistency, but invalidation is hard. Freshness, permissions and compliance requirements complicate “simple” caches.
- Device-aware scheduling improves performance, but increases complexity. You introduce Kubernetes device plugins, operators and topology awareness. It is worth it, but it must be managed.
- Reducing copies can improve latency, but raises platform constraints. Direct data paths reduce CPU overhead, but they come with configuration and compatibility requirements.
- Unifying data services reduces silos, but consolidation needs governance. A unified approach can reduce hop tax, but only if access control, lifecycle policies and ownership are clear.
Future scope: What will matter more next
Over the next 12 to 24 months, I expect four themes to grow:
- AI SLOs become standard: TTFT and TPOT become operational targets, not just benchmark terms.
- Workload placement becomes policy-driven: Placement logic becomes strategic, spanning hybrid footprints.
- More GPU-centric data paths: Fewer CPU copies and less context switching where possible.
- RAG becomes “information supply chain” first: Content-aware approaches and unified data services reduce re-copying and re-governing the same data.
What I would tell a CIO in an elevator pitch
If you want AI to feel fast and reliable, stop treating it like a model deployment and start treating it like a distributed system with strict tail latency expectations.
Measure TTFT and TPOT in percentiles. Map your pipeline fan-out. Make network and storage visible. Then apply disciplined patterns: Isolate lanes, cache aggressively, schedule intelligently, reduce copies in the data path and unify data services where it makes sense.
Your GPUs will thank you, but more importantly, your users will.
This article is published as part of the Foundry Expert Contributor Network.
Want to join?
