Core Metrics for Enterprise Large‑Model Engineering

Compute Engineering – Foundation

Compute Scale Metrics

Available compute scale (EFLOPS): align demand forecasts with planning curves.

Compute Efficiency Metrics

Cost efficiency: unit compute cost (FLOPs/$), power usage effectiveness (PUE), cost per query (CPQ), industry ranking.

Utilization: GPU utilization and resource‑pooling efficiency.

Service Capability Metrics

Task queue latency and average training wait time.

Elastic scaling response time.

Tenant isolation and security indicators.

Application Engineering – Business Scenario Metrics

Business Scenario Scale

Application coverage and landing rate: number of AI scenarios, proportion of business lines covered.

Number of newly covered scenarios.

Business Value Metrics

Process automation rate and efficiency improvement ratio.

Usage, penetration, and adoption rates: daily/weekly active users, frequency of use by business staff, proportion of cases with tangible assistance.

Cost savings amount and ROI.

Model Engineering – Engine

Model Performance

Accuracy and latency.

Model Optimization Metrics

Tuning improvement rate, model compression effect, A/B test results.

Model Deployment Efficiency

Version iteration frequency, release cycle, CI/CD maturity, rollback time.

Model Evaluation

Inference capability, hallucination ratio, energy consumption, completeness of performance monitoring.

Knowledge Engineering – Brain Repository

Knowledge Scale Metrics

Coverage: proportion of business domains covered, number of knowledge points.

Invocation count: times knowledge is called by AI applications.

Utilization: visits, active users, Q&A volume, dwell time.

Knowledge Value Metrics

Quality: correctness, consistency, timeliness.

Asset value: cross‑department and cross‑scenario sharing rate.

Knowledge Service Quality

Retrieval efficiency: search success rate, average search time, recommendation accuracy.

Improvement & update: update frequency, automation proportion.

Knowledge Security

Compliance & safety: knowledge desensitization rate, compliance review pass rate.

Data Engineering – Bloodline

Data Availability Metrics

Quality: completeness, accuracy, consistency, timeliness, standardization level.

Processing performance: response time (request‑to‑completion), throughput (e.g., TPS), resource utilization (CPU, memory, storage, network), concurrency handling.

Data Architecture Metrics

Data lake/warehouse storage utilization.

Data lineage completeness and traceability.

Batch and stream processing performance.

Table/database hotness and corresponding resource efficiency.

Data Application & Value Metrics

Number of data services (standard APIs or product offerings).

Data call rate: times data is used by applications or models.

Business‑user self‑service rate: proportion of usage driven by business staff versus IT.

Data asset reuse rate across departments and scenarios.

Proportion of decisions fully driven by data.

Conclusion

Integrating application, model, compute, knowledge, and data engineering—treating compute as infrastructure, model as engine, knowledge as brain, and data as bloodline—enables enterprises to deliver intelligent experiences.