AI inference demands fundamentally different data infrastructure, says Silk
NVIDIA
Silk
As AI agents move from chatbots to autonomous multi-step workflows, inference workloads are overwhelming traditional cloud storage (AWS EBS) with extreme concurrency, read spikes, and tail latency. Silk advocates for software-defined storage that decouples performance from capacity to handle these demands without relying on fragile read replicas or overprovisioning.
Nvidia CEO Jensen Huang declared the era of 'AI factories,' but Silk warns that the shift from chatbots to autonomous AI agents creates a massive data problem. Inference behaves like OLTP++ with unprecedented concurrency, read spikes, and unpredictable access patterns, quickly exhausting AWS EBS burst credits and causing latency spikes from 1 ms to over 100 ms. Storage bottlenecks, not compute, become the critical failure point. Silk presents a case study of 'FinRetail' where a RAG-based shopping assistant caused a 'success disaster' by overwhelming the storage layer, taking down the entire site. The solution, according to Silk, is a software-defined storage abstraction that decouples application performance from native AWS storage limits, providing consistent sub-millisecond p99 latency even under mixed loads. Silk's symmetric active-active architecture and distributed caching can deliver 20 GiB/s I/O throughput, eliminating the need for fragile read replicas or overprovisioning EC2 compute.
- Сокращения
- AWS = Amazon Web Services
- EBS = Elastic Block Store
- GPU = Graphics Processing Unit
- OLTP = Online Transaction Processing
- RAG = Retrieval-Augmented Generation
- LLM = Large Language Model
- I/O = Input/Output
- IOPS = Input/Output Operations Per Second
- EC2 = Elastic Compute Cloud
- p99 = 99th percentile latency
- p999 = 99.9th percentile latency
- SLA = Service Level Agreement
- KPI = Key Performance Indicator
- AZ = Availability Zone
Source: The Register AI/ML —
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