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How Huawei’s storage–network design targets the KV Cache bottleneck
At the October 2026 Advanced Storage Conference, Huawei reported 7.3× inference throughput from KV Cache offloading in a GLM 5.2 lab test, plus a further 20% efficiency gain from coordinated storage–network I/O. KV Cache stores previously computed attention Key and Value data for reuse.
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Key takeaways
- At the October 2026 Advanced Storage Conference, Huawei reported 7.3× inference throughput from KV Cache offloading in a GLM 5.2 lab test, plus a further 20% efficiency gain from coordinated storage–network I/O.
- KV Cache stores previously computed attention Key and Value data for reuse. Longer contexts and multi turn conversations can expand the cache per request and across a cluster, making its movement between compute, stor...
- Huawei’s reported approach combines direct accelerator access to cached data with storage–network coordination; its October presentation added measurements to a broader design direction it had introduced at HUAWEI CON...
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openai.comCreate a landscape editorial hero image for this Studio Global article: What storage-network convergence approach did Huawei present at the ODCC-organized Advanced Storage Conference in Shanghai on October 22, 20. Article summary: Huawei presented storage–network convergence as a way to reduce the cost of moving KV Cache between compute, storage, and network during agentic AI inference at the October 22, 2026 ASC session in Shanghai; ODCC organize. Topic tags: general, general web. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fake numbers, clic
At the ODCC-organized Advanced Storage Conference in Shanghai on October 22, 2026, Huawei presented storage–network coordination as a way to ease KV Cache bottlenecks in AI inference. The company reported 7.3× throughput in a GLM-5.2 lab scenario after offloading KV Cache, and a further 20% efficiency gain with coordinated I/O scheduling. Those are company-reported results, and Huawei did not publish the full test configuration.5
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Why KV Cache becomes an infrastructure problem
During inference, a model generates Key and Value data as it processes tokens. KV Cache retains that computed data so later token generation can reuse it instead of repeating the same work.47
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The cache grows as context gets longer. In multi-turn interactions, systems may also need to retain or reuse earlier conversational state. As requests and cached data are distributed across a cluster, moving that state between compute, storage, and network can add traffic and pressure on available memory. Huawei’s event coverage described the combined growth in per-request cache, resident cache, and cross-node traffic as pushing cluster KV Cache toward petabyte scale.5
Huawei framed the resulting constraints as three “walls”: limited I/O capability on the compute side, latency as data moves between layers, and insufficient IOPS to serve many cache operations efficiently.5
A compute–storage–network approach
Huawei’s reported approach spans the accelerator, storage, and network rather than treating KV Cache as a memory-capacity issue alone. Event coverage describes a compute-side path in which processors directly initiate cache I/O, progressing from AI CPU and vector-core access toward fine-grained SIMT-level I/O. It also describes storage-side and network coordination as parts of the broader design.5
The performance figures are linked to two steps: direct KV Cache offloading and, on top of that, coordinated storage–network I/O scheduling. The available event reports do not provide enough detail to verify every implementation element or establish that a specific Hyper-Converged Fabric (HCF) scheduling configuration produced the gains. The claims are best understood as results from Huawei’s described lab scenario, not a complete public architecture specification.5
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What Huawei reported—and what remains unknown
In the GLM-5.2 laboratory scenario, Huawei said direct KV Cache offloading produced 7.3× the inference throughput of a conventional memory-based approach. It reported a further 20% improvement in inference efficiency after adding coordinated storage–network I/O scheduling.5
These figures should be treated cautiously. Huawei did not disclose the complete test configuration, which limits outside assessment of the workload, baseline, and how much each design element contributed. The available event coverage does not substantiate the separate claim of more than 30% higher storage-I/O performance, so that figure cannot be treated as verified here.5
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An October update to a broader Huawei storage direction
The October presentation came after Huawei introduced an upgraded Stellar AI Fabric at HUAWEI CONNECT in September. The conference coverage presents the October appearance as a disclosure of KV Cache offloading measurements; Huawei’s September materials also introduced OceanStor M900 Context Memory Storage, designed to extend KV Cache beyond on-chip memory and DRAM into a shared, tiered pool for AI inference.10
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The distinction matters: the October figures add a performance claim to Huawei’s existing storage-and-network direction, but the available sources do not establish that every technical detail discussed in October was identical to the September design. Nor do they document a separate new-product launch at the October conference.5
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At the October 2026 Advanced Storage Conference, Huawei reported 7.3× inference throughput from KV Cache offloading in a GLM 5.2 lab test, plus a further 20% efficiency gain from coordinated storage–network I/O.
What are the key points to validate first?
At the October 2026 Advanced Storage Conference, Huawei reported 7.3× inference throughput from KV Cache offloading in a GLM 5.2 lab test, plus a further 20% efficiency gain from coordinated storage–network I/O. KV Cache stores previously computed attention Key and Value data for reuse. Longer contexts and multi turn conversations can expand the cache per request and across a cluster, making its movement between compute, stor...
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Huawei’s reported approach combines direct accelerator access to cached data with storage–network coordination; its October presentation added measurements to a broader design direction it had introduced at HUAWEI CON...