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Agentic AI workloads usually need a balanced mix of CPUs and GPUs, not simply more of one. GPUs commonly accelerate model inference; CPUs handle much of the orchestration, retrieval, tool execution, state management, and other work between model calls. Add CPU cores when concurrent sessions create substantial CPU-side work. Add GPU capacity when inference latency, throughput, or GPU memory is the constraint. There is no universally valid CPU-to-GPU ratio: benchmark the workflow you intend to run.
Why agentic AI uses both CPUs and GPUs
An agent is an end-to-end workflow, not just a prompt sent to a model. It may route a request, retrieve information, call tools, assemble context, apply guardrails, update state, and then invoke a model again. Those stages have different resource needs.
A GPU is often the main accelerator for larger or latency-sensitive model inference. A CPU commonly runs the agent runtime and supports tool calls, retrieval, context assembly, memory management, validation, and logging. AWS describes these as complementary roles and says compute should be placed where its cost-performance is best: AWS CPU Inference and Orchestration guidance.
The division is not absolute. Smaller quantized models, classifiers, embeddings, and batch scoring can be CPU candidates; GPUs may also be used for other stages. The right choice depends on the model, serving setup, traffic, and service targets.
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When more CPU cores help
Additional CPU capacity can help when many sessions are simultaneously doing work outside model inference. Examples include running sandboxed code, making API or database calls, processing data, searching a vector store, and managing concurrent agent state. Those tasks may compete for CPU time even when the GPU is busy only during inference bursts.
Core count is only one sizing factor. Per-core performance, memory capacity and bandwidth, storage and network I/O, and the actual concurrency and mix of tool calls all affect throughput. AMD notes that high-concurrency sandbox tasks, API calls, or database queries can benefit from core density, while CPUs feeding GPU clusters may instead need strong per-core performance, memory bandwidth, I/O, and networking. These are vendor guidance, not a rule that more cores always improve performance: AMD on agentic AI as an end-to-end workflow.
CPU scaling is most useful when CPU utilization or CPU-side queueing is limiting the workflow. If requests are instead waiting on model generation or GPU memory, adding CPU cores alone is unlikely to solve the problem.
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When GPU capacity matters more
GPU acceleration is often important for larger models and tight inference latency targets. If the model’s inference time dominates end-to-end latency, or the GPU cannot sustain the required generation throughput, more CPU cores will not substitute for GPU resources.
Agent sessions can also make GPU capacity harder to estimate from short, independent requests. NVIDIA characterizes agent inference as potentially lasting minutes or hours, with repeated model calls, growing context, and idle intervals while tools run. Its guidance notes that KV cache—the GPU memory used to retain intermediate context computations—can limit how many sessions a GPU serves concurrently. NVIDIA estimates that a 128K-token context with a 70B model can require roughly 16–32 GB of KV cache, depending on model and serving configuration; this is not a universal memory requirement: NVIDIA Agentic Inference.
NVIDIA also cites 50–500 sequential model invocations for a single agent task, 30K–120K+ tokens as common context windows in its comparison, and a 5–15x GPU overhead multiplier for multi-agent deployments versus single-agent equivalents. These are NVIDIA’s vendor characterizations, not general constants for every application. The practical implication is to measure complete sessions and concurrent context, rather than sizing only for a single model call.
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CPU, GPU, or a split deployment?
A split design often matches the stages of an agent workflow: run orchestration and retrieval on CPU resources, and send generation to GPU-backed inference when the model or latency requirement calls for it. One pattern keeps a small language model and routing layer on CPU for constrained tasks, escalating harder work to a GPU-hosted large model. Another uses CPU resources for retrieval and embeddings, with a GPU-hosted model generating the final response. These are architectural options, not guaranteed best practices for every deployment.
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AWS’s decision table offers useful starting points, but explicitly treats its thresholds as guidance rather than universal rules. The values below are AWS examples, not cross-provider benchmarks or performance guarantees: AWS CPU Inference and Orchestration guidance.
| Workload consideration | CPU-oriented AWS example | GPU/Trainium-oriented AWS example |
|---|---|---|
| Typical work | Quantized 1–8B small language models, embeddings, classifiers, orchestration, retrieval, ETL, and batch scoring | Larger or latency-sensitive online inference, fine-tuning, and training |
| Example p95 latency range | 100–500 ms | Below 50 ms |
| Example endpoint request rate | Under 100 requests per second | Over 100 sustained requests per second |
Those latency and request-rate examples should not be read as thresholds at which a CPU or GPU will necessarily succeed. Model, quantization, context size, software, concurrency, and hardware all affect results. AWS recommends testing candidate CPU families and GPUs with the actual model and traffic pattern, holding quantization, context size, and thread count consistent, then comparing cost per 1,000 queries at the target p95 latency.
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How to find the bottleneck in your workload
- Define the service target. Specify the required p50 and p95 end-to-end latency, throughput, concurrent sessions, and cost per successful task or other useful unit. Include tool and sandbox activity rather than measuring model inference alone.
- Reproduce realistic sessions. Use the model, context lengths, task mix, tool-call patterns, and traffic bursts expected in production. Measure both ordinary requests and long-running or multi-step sessions.
- Measure each stage. Track time and queueing for inference, orchestration, retrieval, tool execution, and context handling. Monitor CPU use, memory pressure, I/O, network, GPU utilization, and GPU memory, including KV-cache use where available.
- Change one resource tier at a time. Compare CPU configurations and GPU options under the same workload and settings. If CPU-side stages or queues are limiting results, test more CPU capacity or faster per-core performance. If inference latency, GPU throughput, or GPU memory is limiting, test GPU-side changes.
- Compare cost at the target. Use cost per successful task or cost per 1,000 queries while meeting the latency and throughput target. A cheaper configuration that misses the service objective is not an equivalent result.
In its June 22, 2026 discussion of hybrid-cloud deployments, AWS describes GPU-accelerated compute for inference and completion generation, with general-purpose instances serving concurrent agent sessions and CPU-bound orchestration. The distinction is a useful architecture pattern, but the implementation details are AWS-specific: AWS hybrid-cloud agentic AI architecture.
What measured workload studies add
A 2026 arXiv preprint, From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems, reports that non-LLM components dominated latency in five of the ten applications it studied. It also reports sandbox working-set memory peaking at 28 GB per session, different resource affinities across inference, retrieval, and sandboxes, and task-latency divergence of up to 32x. These findings describe the paper’s applications and traces; they do not establish universal production requirements.
In design explorations, the paper reports latency reductions of 29–40% with task-aware serving, improvements of up to 4.5x from communication-aware placement, a 4.6x memory-use reduction from state offloading, and removal of 35.2% of redundant search calls with 19.3% lower aggregate search latency through tool-result caching. Those are results reported for its experiments, not guaranteed outcomes for another system. The paper supports a practical lesson: optimization may require changing placement, state handling, or tool behavior—not just buying more compute: arXiv preprint.
Quick Recap
Decision guide
- Test more CPU capacity when concurrent tool calls, sandboxes, retrieval, or agent-runtime queues are the limiting stages.
- Test more GPU capacity when model inference latency or throughput is the limiting stage, or GPU memory constrains concurrent sessions.
- Use a split design when orchestration and retrieval have different scaling needs from generation, or when routing simpler requests to smaller models can reduce load on a larger model.
- Do not size by core count alone. Include per-core performance, memory, I/O, network, model and context size, concurrency, and the cost of meeting the target.
- Do not assume one fixed CPU-to-GPU ratio. The bottleneck can shift as session concurrency, context growth, and tool mix change.
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