Solutions
Four scenarios × eight industries — from training to inference, greenfield to retrofit, across the AI infrastructure lifecycle.
Where does ZK-Storage fit and what value does it bring?
- Use cases
- LLM training / inference, compute centers, brownfield retrofit
- Effective GPU utilization
- ~2-3x higher (S4)
- Total cost
- ~-40%, expansion ~-60% (S9)
- Delivery
- ~6 weeks to volume (S9)
By scenario
One disaggregated platform, an optimal answer for each workload.
Training clusters
Accelerate model loading and checkpoint I/O to shorten iterations and cut idle time on expensive GPUs.
Inference serving
For long contexts and high-frequency switching, KV-cache offload/reuse lifts effective utilization and throughput.
AI centers / domestic stack
Disaggregation plus deep Ascend tuning for sovereign, data-resident infrastructure.
Brownfield retrofit
Speed up in place with no GPU swap and no downtime; with utilization below 60% nationwide, the headroom is vast.S11
WS7000 storage acceleration, purpose-built for AI compute centers
For AI centers / AI factories: disaggregation + end-to-end NVMe + GPU-direct across training, long-context inference, KV cache, agent context sharing and green efficiency — a 70M-IOPS / 300 GB/s / 20 μs-class backbone (vendor spec).
By industry (target coverage)
Target industry coverage representing typical use cases — not signed relationships.
Telecom operators
Operators and their cloud arms building / expanding AI pools; focused on utilization and TCO.
AI centers / IDC
Building or operating compute centers; storage backbone and long-term compute services.
Internet / cloud / AI labs
Training and inference teams facing slow training, costly inference, slow switching.
Financial services
Strong data-residency and sovereignty needs; inference and risk-control acceleration.
Government / sovereign cloud
Self-control and compliance; Ascend-friendly.
Universities / research
Research clusters and public compute platforms; budget-sensitive, utilization-first.
Energy / manufacturing / health
Compute backbone for industry models and industrial AI.
Channel / SI / OEM
Joint solutions and regional delivery with ecosystem partners.
Four business models
Across the lifecycle: sell the box, sell software, retrofit, rent compute.
| Model | Form | Notes |
|---|---|---|
| 1. Appliance sales | Hardware | New AI clusters buy WS5000 directly |
| 2. Software subscription | Recurring | Existing-hardware customers subscribe to the stack |
| 3. Retrofit revenue share | Asset-light | Retrofit in place, share incremental token output |
| 4. Compute service | On-demand | Accelerated storage compute, on demand |
Benchmark it on your own workload
2 live demo units are ready for immediate PoC. Let the data do the talking.
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