Solutions
Five capability lines spanning the AI compute value chain: from making domestic GPUs truly productive to squeezing more output from datacenters already built. Every solution follows the joint-test-first, gate-based acceptance methodology — with stop-loss if gates are missed.
Domestic GPU Enablement & Joint Optimization
Source-level inference-stack adaptation and measured validation across AMD MI308X, Huawei Ascend 910B, MetaX N260 and other platforms — turning domestic / non-NVIDIA accelerators into production capacity.
Storage Acceleration (KV Cache Tiering)
FX series all-flash NVMe-oF arrays plus a KV-cache tiering software stack: signed benchmarks on a 480B model in production deployment form show throughput +29–40% and TTFT −26–32%.
AI Datacenter Construction
Complete build-out plans from 128-GPU joint tests to thousand-GPU datacenters: Clos network BOM, three-tier storage with KV tiering, power/PUE, and a fully reproducible Python TCO model.
Datacenter Efficiency Optimization
Efficiency mining for existing clusters: model-switch effective TPS, concurrent loading, checkpoint writes, utilization modeling — improve output first, add GPUs later.
Software Development & New-Requirement Delivery
Source-level inference-stack engineering plus developer resources at scale: from upstream patches to fast delivery of new industry needs (video generation, agent platforms, private inference appliances).