Big Blue’s Redbook on Storage Scale KV Cache Management

Why It Matters

By extending cache beyond GPU memory, enterprises can dramatically cut inference latency and operational costs, unlocking scalable GenAI services without over‑provisioning GPUs.

Key Takeaways

• IBM Redbook details KV cache architecture using Storage Scale ECE.
• Multi‑layer cache reduces HBM eviction, improves inference latency.
• 56× TTFT speedup at 130k token prompts vs GPU‑only.
• Throughput rises 22× to 4.26 RPS, 95% faster processing.

Pulse Analysis

Enterprises deploying generative AI face a fundamental bottleneck: the limited capacity of GPU high‑bandwidth memory (HBM) to retain large context windows. When prompts exceed a few thousand tokens, repeated recomputation erodes latency and inflates GPU costs. IBM’s Storage Scale, paired with Nvidia’s Dynamo engine, tackles this by offloading KV cache data across a hierarchical storage stack, allowing the system to keep active context in HBM while spilling inactive data to progressively slower but larger tiers.

This approach mirrors traditional memory hierarchies but is optimized for the massive, transient datasets characteristic of multi‑turn assistants, retrieval‑augmented generation, and autonomous agents.

5), and an external shared storage tier powered by IBM Storage Scale Erasure Coding Edition on Supermicro servers (G4).

Each layer balances latency and capacity, with the G4 tier handling non‑critical cache such as dormant session state. By intelligently moving data based on access patterns, the system maintains near‑flat time‑to‑first‑token (TTFT) even for 130 k‑token prompts, delivering a 56× speedup over a GPU‑only baseline. 26 RPS, a 22× increase, while total processing time for 200 requests drops 95 %.