# Solutions: Storage Acceleration, Enablement, AI Datacenters

> Five lines of work from Xinyuanyi: KV cache tiering, enablement for domestic and non-NVIDIA accelerators, datacenter efficiency, datacenter construction and inference-stack software, each tied to report IDs.

URL: https://vuke.top/en/solutions · Updated November 12, 2026

Starting from one storage tier, we work upward into inference-stack software and downward into datacenter planning and construction. Each line of work points to specific report IDs.

## [Storage Acceleration: KV Cache Tiering](https://vuke.top/en/solutions/storage-acceleration)

Add an FX all-flash NVMe-oF tier beyond GPU memory and host RAM, and let vLLM + LMCache write overflowing KV cache into it and read it back on a hit. On a 480B model in production topology this yields 29–40% more throughput and 26–32% lower TTFT than a single local NVMe drive [R2](https://vuke.top/en/evidence/R2) [R3](https://vuke.top/en/evidence/R3).

Source: [R1](https://vuke.top/en/evidence/R1) [R2](https://vuke.top/en/evidence/R2) [R3](https://vuke.top/en/evidence/R3) [R4](https://vuke.top/en/evidence/R4) [R9](https://vuke.top/en/evidence/R9)

## [Enablement for Domestic and Non-NVIDIA Accelerators](https://vuke.top/en/solutions/domestic-compute)

We adapt the inference stack at source level and publish a separate measured report for each platform: 8 × AMD MI308X [R1](https://vuke.top/en/evidence/R1), 8 × Huawei Ascend 910B [R9](https://vuke.top/en/evidence/R9) and MetaX N260 [R5](https://vuke.top/en/evidence/R5). Figures from different platforms are labeled separately and never merged into one headline.

Source: [R1](https://vuke.top/en/evidence/R1) [R5](https://vuke.top/en/evidence/R5) [R6](https://vuke.top/en/evidence/R6) [R7](https://vuke.top/en/evidence/R7) [R8](https://vuke.top/en/evidence/R8) [R9](https://vuke.top/en/evidence/R9)

## [AI Datacenter Efficiency Optimization](https://vuke.top/en/solutions/efficiency-optimization)

Cut the waiting first, then talk about adding GPUs. At 20 model switches per hour, effective compute utilization rises from 46.7% to 62.8% [R1](https://vuke.top/en/evidence/R1).

Source: [R1](https://vuke.top/en/evidence/R1) [R5](https://vuke.top/en/evidence/R5)

## [AI Datacenter Construction](https://vuke.top/en/solutions/datacenter-construction)

From a 128-GPU joint test to a thousand-GPU datacenter, we deliver plans you can recompute: network BOM, three-tier storage with KV tiering, power and PUE, and TCO are all modeled in Python for you to rerun with your own parameters.

Source: [R1](https://vuke.top/en/evidence/R1) [R2](https://vuke.top/en/evidence/R2) [R3](https://vuke.top/en/evidence/R3)

## [Inference-Stack Software Development](https://vuke.top/en/solutions/software-development)

We change the inference stack at source level and hand the changes over as they are: the patch, the orchestration scripts and the raw data are all in R8 [R8](https://vuke.top/en/evidence/R8).

Source: [R1](https://vuke.top/en/evidence/R1) [R6](https://vuke.top/en/evidence/R6) [R7](https://vuke.top/en/evidence/R7) [R8](https://vuke.top/en/evidence/R8)