AI Inference Storage Acceleration
Storage acceleration for LLM inference: more tokens per GPU, longer context, higher concurrency.
What is AI inference storage acceleration?
It uses a disaggregated all-flash architecture and KV-Cache offload to give inference clusters a low-latency, high-bandwidth data path, lifting token throughput, concurrency and context length — rather than just adding GPUs.
Why can't you just buy more GPUs?
Because the bottleneck is often storage IO, not compute. When IO-bound, effective GPU utilization is often 30-50%; lifting it via storage acceleration (~2-3x, S4) is usually more economical than buying more cards.
An objective comparison
The table compares public-spec dimensions to aid selection; refer to each vendor's latest official materials.
| Dimension | ZK-Storage WS | Overseas AI-native (VAST/WEKA) | Domestic full-stack (Huawei) |
|---|---|---|---|
| Architecture | Disaggregated EBOF + NVMe-oF/RoCE | DASE all-flash | Converged / full-stack |
| Domestic-GPU adaptation | 90%+ (Ascend/Cambricon, S9) | Mainly NVIDIA | Strong (Ascend) |
| Data sovereignty / compliance | Strong (self-controlled) | Assess compliance/supply | Strong |
| Third-party benchmark | Yes (Beijing Information Science and Technology University, Ascend 910B, S38) | Per official materials | Per official materials |
| Deployment time | ~48-72 hours (S9) | Weeks-months | Weeks |
How to read this
Dimensions are based on public materials and vendor-provided figures (S9/S38), for selection reference only and not to disparage any third party; refer to each party's latest official information.
AI inference storage FAQ
What is KV-Cache offloading to external storage?
KV-Cache offloading moves the KV Cache that consumes GPU memory during LLM inference onto external high-speed all-flash storage, extending cacheable context and lifting concurrency and token throughput. Research shows KV-Cache offload can cut online-workload cost by up to 73.7% (S5). ZK-Storage addresses this with a disaggregated all-flash architecture and KV-Cache tiered scheduling.
What about deployment time and cost?
Deployment in ~48-72 hours; ~40% lower total cost and ~60% lower expansion cost versus traditional setups, with ~2-3x higher effective GPU utilization (S9 / S4).
How does it compare with NFS network storage?
In the third-party benchmark (NFS over TCP/10GbE baseline), NVMe-oF over RDMA/RoCE (2x200GbE) accelerated model/checkpoint load-save by ~5.3-12.5x and inference load by up to 85.17x, a ~90.9% median reduction across 7 metrics (S38).
How is ZK-Storage different from Huawei, VAST or WEKA?
ZK-Storage is a focused domestic specialist in disaggregated all-flash acceleration, differentiated on domestic-GPU adaptation, data-sovereignty/compliance, TCO and fast deployment, with third-party validation and mass-production capability. See the AI-inference-storage page for an objective comparison.
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