How to Choose AI Infrastructure for Agents: GPUs, CPUs, Memory and Deployment Platforms

What Happened

Recent NVIDIA engineering posts put the focus on the systems around the GPU. Agents alternate between GPU-based reasoning and CPU-heavy work such as search, database queries and code execution [1]. For distributed training, a GPU can slow an entire job while still passing basic health checks [3]. NVIDIA also identifies high-bandwidth memory (HBM) pressure from model weights, idle-session KV caches and repeated prefixes [5][6].

On the platform side, Databricks says Spark 4.3 adds stateful processing to Real-Time Mode [8]. The available description does not establish a performance result. The available Databricks Apps excerpt likewise contains too little detail to support a scaling recommendation [2].

Why It Matters to Businesses

GPU count alone is a poor buying metric. Agent response time depends on CPU execution, retrieval, model inference and the handoffs between them [1]. Training capacity can be lost to a single slow device [3], while inference capacity depends partly on how efficiently expensive HBM is used [6]. Buyers should compare completed work per dollar at their required latency, not accelerator specifications in isolation.

Kimbodo Engineering Perspective

We would treat NVIDIA and AMD accelerators, Intel and other CPU options, and AWS, Google Cloud and Azure offerings as candidates for workload-specific tests—not assume one vendor wins every workload. The choice between managed AI services and self-operated clusters is a trade-off: managed services can reduce operational ownership; direct cluster control can give teams more say over scheduling, model serving and diagnostics. Neither choice removes the need to measure application-level performance.

We would also separate the data platform decision from the inference decision. Databricks or Snowflake may be part of the data path, while Cloudflare or another edge platform may sit on the request path. Each belongs in an evaluation only if it solves a measured requirement. NVIDIA’s internal operations agent illustrates a useful pattern—correlating compute, network, hardware and scheduling signals—but it is not evidence that automated diagnosis replaces SRE judgment [9].

How We Would Implement It

• Benchmark representative flows: measure end-to-end agent latency, throughput and cost, including tool calls and database queries—not just tokens per second [1].
• Test capacity under contention: record GPU memory use, KV-cache behavior, CPU saturation and queue time across realistic concurrency levels [5][6].
• Qualify clusters before and after changes: run workload-level GPU performance tests after node replacement or driver patches, because basic health checks can miss slow devices [3].
• Instrument the full path: link request traces to inference, retrieval, network and scheduler metrics so operators can investigate regressions across systems [9].
• Deploy in stages: compare a managed-service baseline with a controlled cluster or self-hosted model only where the expected cost, latency or control benefit justifies operating it.

Risks, Costs and Security

HBM capacity and pricing can constrain deployment plans, so budgets should account for concurrency and memory use as well as GPU hours [5][6]. Self-operated clusters add patching, performance qualification and incident-response work [3]. For agents, tool access also expands the security boundary: use least-privilege credentials, isolate code execution, approve high-impact actions and retain auditable traces. Validate vendor performance claims against production-like workloads before committing to hardware or a cloud contract.

Sources

1. [1] Vera Delivers the Best of Both Worlds: Highest Throughput and Fastest Agents
2. [2] A builder’s guide to scaling Databricks Apps
3. [3] Configure, Certify, and Operate Kubernetes GPU Clusters with NVIDIA DSX OS
4. [5] More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge
5. [6] Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
6. [8] Introducing Apache Spark® 4.3 – Now available in Databricks Runtime 19
7. [9] How NVIDIA Uses AI Agents to Streamline AI Factory Operations