Technical Briefing Deck · ZK-Storage vs NVIDIA
An HTML slide deck for technical exchange: an objective comparison of disaggregated all-flash storage with NVIDIA's inference paradigm, with NVIDIA's own descriptions and links. Full-screen and PDF export ready.
What is this?
This is ZK-Storage's HTML slide deck (mock-PPT) for technical exchange. It maps ZK-Storage's disaggregated all-flash storage stack against NVIDIA's inference paradigm (Dynamo Disaggregated Serving, KVBM tiered KV-cache offload, GPUDirect Storage, NIXL), point by point, and quotes NVIDIA's own descriptions of these analogous technologies, with links.
The stance is fair, non-disparaging and verifiable: ZK figures come from a single source (third-party benchmark S38, vendor spec S9), and NVIDIA descriptions are from its official public docs. The two are “same paradigm, different layers” and complementary — a disaggregated all-flash storage base is part of that paradigm, and ZK-Storage provides it for sovereign compute (Ascend / domestic GPUs).
Mapped to NVIDIA
Disaggregation ↔ Dynamo, KV-cache offload ↔ KVBM, GPU path ↔ GPUDirect Storage, data path ↔ NIXL.
Verifiable · fair
ZK figures labeled S9/S38; NVIDIA descriptions link to official docs. Refer to each party's latest official information.
Presentable · PDF export
16:9 slides, arrow/click navigation, press O for overview; browser “Print → Save as PDF” yields landscape one-slide-per-page.
ZK-Storage vs NVIDIA · objective comparison
The comparison table from the deck (rendered from the same source for search and citation); refer to each party's latest official info.
| Dimension | ZK-Storage WS5000 | NVIDIA equivalent (official) |
|---|---|---|
| Layer | All-flash storage appliance (hardware base) | Inference / IO software framework (Dynamo·NIXL·GDS) |
| Disaggregation | Hardware EBOF + NVMe-oF/RoCE | Dynamo Disaggregated Serving (prefill/decode split) |
| KV-cache offload | KV-cache tiered scheduling (mem↔flash) | KVBM tiers G1→G4 (GPU→CPU→SSD→remote) |
| GPU direct path | GPUDirect path + NVMe-oF | GPUDirect Storage (GPU↔NVMe/NVMe-oF DMA) |
| Primary compute fit | Domestic GPU / Ascend 90%+ (S9) | Mainly the NVIDIA GPU ecosystem |
| Data sovereignty | Strong (self-controlled) | Assess per deployment / compliance |
| Third-party benchmark | Yes (Beijing Information Science and Technology University, Ascend 910B, S38) | Per official / partner materials |
| Relationship | Complementary: a sovereign storage base for the paradigm | Open to third-party storage (WEKA / Dell, etc.) |
How to read this
An objective dimension-by-dimension reference, not a disparagement of any third party. ZK-Storage is an all-flash storage appliance (hardware base); NVIDIA provides inference / IO software frameworks — the two are complementary. ZK figures are labeled vendor spec (S9) / third-party benchmark (S38).
NVIDIA's own descriptions of the analogous technologies, with links
All quotes are drawn faithfully from NVIDIA official docs and open-source repos, click to verify.
- NVIDIA GPUDirect Storage（Magnum IO GDS）
“GPUDirect Storage enables a direct data path between local or remote storage, such as NVMe or NVMe over Fabric (NVMe-oF), and GPU memory. It avoids extra copies through a bounce buffer in the CPU’s memory, enabling a DMA engine near the NIC or storage to move data on a direct path into or out of GPU memory — all without burdening the CPU.”
NVIDIA Developer · GPUDirect · GPUDirect Storage Overview Guide - NVIDIA Dynamo · 分离式推理服务（Disaggregated Serving）
“Disaggregated serving runs prefill and decode on different devices so each can be scaled and parallelized independently. It required three capabilities: scheduling, memory management for KV cache offloading and onboarding, and low-latency data transfer to move KV cache between nodes and across the memory hierarchy.”
NVIDIA Dynamo · Introduction · ai-dynamo/dynamo (GitHub) - NVIDIA Dynamo KVBM · KV Cache 分层卸载
“The KV Block Manager (KVBM) offers a unified memory API spanning GPU memory, pinned host memory, remote RDMA-accessible memory, local/distributed SSDs, and remote file/object/cloud storage. Offloading KV cache from HBM to cheaper tiers (G1 GPU → G2 CPU → G3 SSD → G4 remote) improves TTFT, reduces TCO and enables longer context.”
NVIDIA Dynamo · KVBM - NVIDIA NIXL · 推理数据传输库
“NIXL (NVIDIA Inference Xfer Library) provides a non-blocking API for high-performance, vendor-agnostic data movement, transferring KV caches across GPU memory, CPU memory and storage tiers (SSD / remote) for use cases such as disaggregated KV cache movement, long-context storage and model-weight transfer.”
NVIDIA Technical Blog · NIXL · ai-dynamo/nixl (GitHub)
Live preview
The deck is embedded below; use arrow keys / click to navigate. Full-screen recommended; export PDF from inside the deck.
Open the deck full-screen (press ⎙ to export PDF) ↗
Note: the deck is bilingual and follows the site language; all figures are traceable and reproducible.
Benchmark it on your own workload
2 live demo units are ready for immediate PoC. Let the data do the talking.
Last updated：