IBM Storage Scale parallel file system supports distributed KV cache management paired with NVIDIA Dynamo, catering to large-scale AI inference scenarios with massive context workloads.
IBM has released an official Redbook titled Context Without Limits: A High-Performance KV Cache Platform for Large-Scale AI Inference, delivering a complete validated reference architecture for this joint solution. The integrated stack combines Supermicro Petascale Storage Servers, NVIDIA Spectrum-X Ethernet networking, and IBM Storage Scale Erasure Coding Edition (ECE) to build a high-performance shared storage tier for AI inference. As authoritative technical documents published by IBM ITSO (International Technical Support Organization), IBM Redbooks offer hands-on, in-depth deployment guidance for enterprise-grade IBM infrastructure products.
Co-authored by engineering teams from IBM, Supermicro and NVIDIA, the Redbook addresses a core pain point of long-context AI workloads. Use cases including multi-turn dialogue assistants, RAG retrieval applications and autonomous agent pipelines generate massive KV cache data inside GPU HBM. Once cached data is evicted from limited HBM resources, repeated recomputation will trigger severe latency rises, making persistent cross-request KV cache storage indispensable.
The solution adopts a five-tier hierarchical KV cache architecture covering different latency and capacity demands:
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G1 Layer: GPU node local HBM
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G2 Layer: CPU node system DRAM
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G3 Layer: Direct-attached local SSD
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G3.5 Layer: Pod-level shared flash storage, fronted by NVIDIA BlueField DPUs with direct interconnection to GPU server DPUs
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G4 Layer: External cross-Ethernet shared storage pool connected to all GPU compute servers
Covering end-to-end memory and storage hierarchy, this multi-tier setup delivers continuous latency and capacity gradients. It enables NVIDIA Dynamo to conduct intelligent cache placement, automatic eviction and dynamic data reloading across the whole storage stack, adapting flexibly to varied workload access patterns and total infrastructure cost budgets.
Deployed on Supermicro Petascale Storage Servers, Storage Scale ECE serves as the G4 cold cache tier. It is optimized for non-latency-sensitive KV cache data, including inactive multi-turn conversation states, shared agent context data and historical query records that do not require instant response.
According to test results recorded in the Redbook, this production-ready reference architecture effectively accelerates generative AI and agentic AI inference services. In single-request TTFT (Time To First Token) tests compared with standalone GPU servers without external Storage Scale KV cache, the integrated system maintains stable TTFT regardless of prompt length changes. It achieves a 56x speedup under 130k-token input sequences and completely eliminates inference latency fluctuations caused by extended prompt lengths.
Under concurrent multi-user inference pressure, the solution achieves dramatic performance improvement: request throughput surges from 0.19 RPS to 4.26 RPS, marking a 22x throughput boost. Meanwhile, the total processing time for 200 inference requests drops by 95%, greatly lifting GPU utilization efficiency and overall inference cluster scalability.
The stack also maintains robust performance under harsh noisy-neighbor stress tests. With four client ends generating sustained 200 GB/s competing network I/O traffic, the integrated system still stably runs at 3.6 RPS, finishing all 200 inference requests within 55.56 seconds. Its throughput remains 18x higher than the baseline GPU-only recomputation architecture.
The research team concluded in the Redbook: “For enterprises aiming to maximize ROI on expensive GPU hardware investments, this verified integrated architecture provides a straightforward, production-ready approach to boosting inference throughput, cutting end-to-end latency, supporting higher service concurrency, and building more cost-effective large-scale AI inference infrastructure.”
Keywords: SUPERMICRO, IBM Storage Scale, NVIDIA Dynamo
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
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