Why Massive GPU Farms Still Fail to Deliver Enterprise‑Ready AI—and How Jiuzhang’s AI Factory Solves It

By March 2026 China’s daily token usage exceeded 140 trillion, a growth of more than a thousand‑fold in two years, signaling that AI has moved from the lab to production lines. Yet enterprises report a paradox: general large models excel at chat‑style responses but repeatedly fail when embedded in real‑world ticketing or approval processes, because they are trained to generate tokens rather than reliably execute tasks.

The Execution Gap

Jiuzhang Yunji’s founder Fang Lei describes this as a shift from competing on model excellence to competing on AI productivity. The core problem is not model size or compute bandwidth, but the lack of an industrial‑grade pipeline that can turn token generators into task‑oriented professionals.

AI Factory Strategy

The AI Factory consists of two tightly coupled components: the Training Factory , which refines general models into "professional model assets" via large‑scale reinforcement learning (RL), and the Token Factory , which packages these assets into consumable services that can be invoked like electricity.

Training Factory Architecture

Three engineering chasms must be crossed to industrialize RL:

Supply‑stable ten‑thousand‑GPU compute : RL training requires continuous sampling and updating across thousands of tasks, demanding a cluster stability previously only seen in top‑tier labs.

Massive agent simulation scheduling : Parallel rollout and parameter updates have divergent, dynamic compute patterns that static schedulers cannot handle, requiring fault‑tolerant, checkpoint‑aware orchestration.

From research code to production systems : Managing and iterating reward functions for myriad specialized tasks, and building a repeatable evaluation loop, needs deep engineering experience.

Jiuzhang’s solution is a full‑stack system built on five interlocking capabilities:

Elastic compute : GPU resources scale up in seconds and release automatically, with priority‑based pre‑emptive scheduling.

Hybrid scheduling : Training, inference, and fine‑tuning share a unified scheduler that bypasses failed nodes and resumes from checkpoints.

Network optimization : Zero‑copy, high‑speed inter‑node links minimize data movement overhead.

Storage optimization : Pre‑loading and cache warm‑up eliminate the classic "compute waiting for data" bottleneck.

Multi‑tenant queuing : Isolated workloads share the same cluster, with urgent jobs inserted ahead of background tasks, boosting overall utilization.

These capabilities yielded a 100 % improvement in training efficiency and a 50 % increase in GPU utilization compared with industry baselines, as verified by the China Academy of Information and Communications Technology.

Reinforcement‑Learning Training Stack

The stack supports mainstream RL algorithms (PPO, DPO, GRPO, RLHF, RLAIF) in parallel, allowing domain‑specific algorithm selection. Its reward‑modeling engine automatically generates and iterates reward functions for thousands of professional tasks, turning "will answer" models into "will do" models. Tool‑calling and multi‑step execution enable models to invoke external services, decompose complex goals, and self‑correct after failures.

Inference OS

Rather than patching existing inference frameworks, Jiuzhang introduces an Inference OS centered on state orchestration . The system treats inference as a memory‑centric state machine, akin to a database, focusing on reuse‑plan decisions (what state to keep, when to pre‑fill, when to decode) to close the >10× performance gap between theoretical token throughput (≈1000 tokens/s on an 8‑GPU server) and actual decode speeds (tens of tokens/s).

Key innovations include:

DingoFS Connector: prefix‑hash sharding, zero‑copy RDMA + io_uring, raising KV cache hit rates to 60‑90 % and boosting TPS 10× over HBM‑only baselines, with 5.3× over leading cross‑node L2 caches.

PD (Prefill‑Decode) scheduling: dedicated hardware pools for prefill and decode improve TPS by an additional 2‑4×.

Ahead‑of‑Time compiled persistent kernels eliminate kernel‑to‑kernel sync, delivering a 4× speedup over conventional engines.

Energy‑aware scheduling: real‑time electricity pricing and green‑energy signals guide task placement, cutting inferred carbon emissions by ~47 %.

Token Factory and Professional Tokens

Professional model assets flow from the Training Factory into the Token Factory, which exposes them as "special‑alloy" services that can be consumed on demand. Tokens are categorized into three layers:

Consumer‑grade Tokens : high‑throughput, low‑latency services for mass‑market AI apps.

Professional‑grade Tokens : encapsulate industry know‑how and compliance logic, delivering efficiency, risk control, and decision support.

Frontier‑grade Tokens : support R&D‑intensive scenarios such as new‑material discovery, drug design, and city‑scale optimization.

Strategic targets announced at the 2026 Global Intelligent Computing Summit include a 10⁵‑P training cluster, daily processing of 10 trillion high‑quality tokens, a 1000× overall cost reduction, and a ecosystem of 1000+ models and applications.

Global Vision

Jiuzhang’s infrastructure already spans major Chinese regions and overseas markets (Southeast Asia, Middle East). The "Southern AI Prometheus" plan aims to compress the build time of resilient compute bases for developing nations from years to months, delivering low‑margin, high‑impact AI capacity worldwide.

Conclusion

By closing the execution gap with reinforcement learning, providing a five‑capability training backbone, and redefining inference as a state‑oriented OS, Jiuzhang’s AI Factory creates a closed‑loop industrial chain that turns "can talk" models into "can work" services, positioning AI as a measurable, billable, and scalable production utility for the next decade.