At WAIC 2026 on July 17, Chinese AI infrastructure company Meta-Stone (是石科技) unveiled its first "domestic Token optimization factory — Vectron (拓元)". The core idea is straightforward: turn every watt of compute into stable, efficient tokens. Specifically, it lands on three goals — under the same compute budget, produce more effective tokens; under the same deployed model, deliver faster inference; on the same GPU memory, run long contexts more stably. On the technical side, Vectron compresses the entire AI inference stack into "one system", layered in four optimization stages: a task-adaptive layer that matches the optimal compute path per request profile; an operator-library layer doing hardware-specific kernel fusion and compilation; a model-and-inference-framework layer that supports domestic chips and large models with heterogeneous deep tuning; and a heterogeneous-cluster-scheduler layer that breaks geographic and chip silos into a unified pool. The five technical breakthroughs are what make it worth watching: result-aware KV Cache compression (breaking the limits of static attention), training-free all-modality inference token compression, long-context post-training optimization (with far less training data than Meta's approach), meta-reward-based deep-reasoning optimization, and a long-horizon task memory mechanism for agents. Together, this combination takes aim at almost every major LLM inference pain point — KV cache, token inflation, long context, deep reasoning, agent memory. As of now, Vectron is compatible with 10+ domestic compute chips, adapted to 20+ mainstream models, achieves daily token throughput in the hundreds of billions, and serves top-tier internet and large-model vendors along with high-end manufacturing, aerospace, and biopharma customers. China's intelligent compute scale has now crossed 1,000 EFLOPS, and "can't fully utilize, can't afford, can't run fast" is the industry's common pain point — Vectron's "efficiency-first" path stands in sharp contrast to the scale-competition route of simply stacking GPUs. Meta-Stone's core team comes from Tsinghua University, with engineering experience at national-level compute centers — one of the few Chinese companies that has accumulated both HPC and commercial AI application cases. In a moment of structural compute supply-demand imbalance, making "taming compute" a core competency may be more pragmatically meaningful than simply counting cards.