Guidelines for Sizing GPU/CPU Clusters, Scaling Vector Databases, and Designing High-Performance Inference Systems

As enterprises operationalize artificial intelligence at scale, infrastructure planning becomes one of the most critical—and most underestimated—determinants of success. AI workloads differ fundamentally from traditional enterprise compute. They are bursty, stateful, latency-sensitive, and highly variable in cost and performance characteristics.

This white paper provides a practical framework for capacity planning and performance engineering of enterprise AI workloads. It covers how to size GPU and CPU clusters, scale vector databases, design inference architectures, define latency and throughput service levels, and implement distributed inference strategies that balance performance, cost, and resilience.

The objective is to move enterprises from reactive infrastructure provisioning to deliberate, architecture-driven AI performance engineering.

1. Why AI Capacity Planning Is Fundamentally Different

Traditional capacity planning assumes predictable workloads, stable performance curves, and relatively linear scaling. AI systems violate all three assumptions.

AI workloads vary by model type, context length, document size, concurrency, and orchestration logic. A single user request can fan out into multiple inference calls, retrieval operations, and validation steps. Performance is not governed by CPU utilization alone but by memory bandwidth, GPU availability, interconnect latency, and storage throughput.

As a result, AI systems must be designed with performance as a first-class architectural concern rather than an operational afterthought.

2. Understanding AI Workload Profiles

Before sizing infrastructure, enterprises must classify AI workloads.

Broadly, AI workloads fall into four categories: training, fine-tuning, batch inference, and real-time inference. Most enterprises do not train foundation models but increasingly perform fine-tuning, embedding generation, document processing, and real-time inference at scale.

Document AI and agentic workflows introduce additional complexity. Workloads are heterogeneous, combining OCR, embedding generation, vector search, reasoning, and orchestration in a single transaction. Capacity planning must account for the entire pipeline, not just the model call.

3. Sizing GPU and CPU Clusters

3.1 GPU Sizing Principles

GPUs are typically the scarcest and most expensive resource in AI systems. Over-provisioning increases cost; under-provisioning creates latency spikes and service instability.

Key factors influencing GPU sizing include model size, precision (FP16, BF16, INT8), batch size, concurrency targets, and average context length. Memory capacity is often the limiting factor before raw compute.

Enterprises should size GPU clusters based on peak concurrent inference rather than average load, while using scheduling and batching strategies to improve utilization.

3.2 CPU Sizing and Hybrid Compute

Not all AI tasks require GPUs. Pre-processing, orchestration, policy enforcement, lightweight inference, and post-processing are often CPU-bound.

Separating GPU-bound and CPU-bound workloads improves efficiency. CPUs should be sized to handle request fan-out, orchestration logic, vector queries, and governance checks without becoming a bottleneck.

A common failure mode is GPU idling due to insufficient CPU-side throughput.

4. Inference Cluster Design

4.1 Real-Time vs Batch Inference

Real-time inference requires strict latency guarantees and typically prioritizes concurrency over throughput. Batch inference prioritizes throughput and cost efficiency.

Enterprises should isolate these workloads into separate inference pools. Mixing batch and real-time traffic in the same cluster leads to unpredictable latency and SLA violations.

4.2 Horizontal vs Vertical Scaling

Vertical scaling (larger GPUs) simplifies architecture but increases blast radius. Horizontal scaling (more nodes) improves resilience and allows finer-grained scheduling.

Most enterprises benefit from horizontally scalable inference clusters with intelligent routing and load balancing.

5. Latency and Throughput Engineering

Latency in AI systems is multi-dimensional. It includes request parsing, orchestration, retrieval, model inference, validation, and response assembly.

Enterprises should define latency budgets for each stage rather than treating inference latency as a single metric. This enables targeted optimization and avoids over-investing in the wrong layer.

Throughput must be measured in business-relevant units such as documents per hour, decisions per second, or cases per day—not raw token throughput alone.

6. Scaling Vector Databases

Vector databases are often the hidden performance bottleneck in AI systems.

Key design considerations include embedding dimensionality, index type, query concurrency, and update frequency. As vector volumes grow, naive scaling approaches lead to degraded recall or unacceptable latency.

Enterprises should partition vector indexes by domain, lifecycle, or sensitivity rather than relying on monolithic indexes. Read-heavy and write-heavy workloads should be isolated to avoid contention.

Caching frequently accessed vectors and query results can dramatically reduce latency and cost.

7. Distributed Inference Strategies

7.1 Model Parallelism vs Data Parallelism

Large models may exceed the memory capacity of a single device. Model parallelism distributes model layers across devices, while data parallelism replicates models across nodes.

Enterprises should favor data parallelism for inference whenever possible, as it simplifies scaling and improves fault isolation.

7.2 Multi-Model Routing

Different tasks require different models. Routing requests dynamically based on complexity, sensitivity, or cost allows enterprises to reserve high-end models for high-value cases.

Multi-model routing reduces overall load on premium inference clusters and improves system stability.

7.3 Edge and Hybrid Inference

Latency-sensitive or regulated workloads may require inference closer to the data source. Hybrid architectures distribute inference across on-prem, edge, and cloud environments under centralized governance.

This approach improves performance while meeting data residency requirements.

8. Performance Isolation and Resilience

AI systems must be designed to fail gracefully.

Key strategies include workload isolation by business unit or priority, rate limiting, circuit breakers, and fallback models. When premium models are unavailable, the system should degrade predictably rather than fail outright.

Performance isolation ensures that a surge in one workload does not cascade across the enterprise.

9. Observability and Continuous Optimization

Capacity planning is not a one-time exercise.

Enterprises must instrument AI systems with fine-grained observability, tracking queue depth, GPU utilization, latency distributions, error rates, and cost per transaction.

This data feeds continuous optimization loops, allowing model mix, batching strategies, and infrastructure allocation to evolve over time.

10. Common Failure Patterns

Enterprises that struggle with AI performance often exhibit the same issues: GPU underutilization due to orchestration bottlenecks, vector databases becoming latency chokepoints, over-provisioning for average load, and lack of workload isolation.

These failures are architectural, not vendor-specific.

11. Designing for Sustainable Scale

Sustainable AI performance requires three principles. First, decouple orchestration from inference so capacity can be optimized independently. Second, treat models as replaceable components with explicit performance profiles. Third, design infrastructure around business SLAs rather than technical benchmarks.

When these principles are applied, AI systems scale predictably without runaway cost.

Conclusion

Capacity planning for AI workloads is no longer an infrastructure exercise. It is a strategic capability that determines whether AI systems deliver consistent value or become operational liabilities.

Enterprises that invest in performance engineering, intelligent sizing, and distributed inference architectures gain predictable latency, controlled cost, and long-term resilience.

Those that do not will continue to chase performance problems reactively.

In the AI era, performance is not optimized after deployment.
It is designed from the first architecture diagram.