Solutions
Five capability lines covering the AI compute value chain — from making domestic accelerators genuinely productive to squeezing more effective throughput out of existing datacenters. Every engagement follows a gated validate-first methodology: if a gate fails, the project stops.
Domestic AI accelerator enablement & co-optimization
Source-level inference-stack adaptation and measured validation across AMD MI308X, Huawei Ascend 910B and MetaX N260 — making non-NVIDIA compute genuinely productive.
Storage acceleration (tiered KV cache)
FX all-flash NVMe-oF arrays plus a tiered KV-cache software stack: signed benchmarks on a production 480B LLM deployment show 29–40% higher throughput and 26–32% lower TTFT.
AI datacenter construction
Complete build-out plans from 128-GPU pilots to kilocard-scale datacenters: Clos network BOM, three-tier storage with KV offloading, power/PUE, and a reproducible Python TCO model.
Datacenter efficiency optimization
Efficiency mining for existing clusters: model-switch effective TPS, concurrent model loading, checkpoint writes and utilization modeling — improve efficiency before buying more GPUs.
Software development & custom delivery
Source-level inference-stack engineering plus large-scale developer resources: from upstream patches to new-market deliveries such as AI video pipelines and private inference appliances.
Evidence badges (R1–R9) reference the signed test reports in the evidence library.