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Neither CPU-only nor GPU-accelerated servers are universally better for agentic AI. CPUs run the agent loop, tools, application state, and data handling; GPUs can speed up model inference when their compute and memory fit the model and workload. Choose by measuring the complete agent task—including repeated model calls and tool execution—not by comparing one isolated inference request.
What changes when a server runs an AI agent?
An agent commonly alternates between model inference and other work: it may interpret a request, call a tool, process the result, update its state, and ask the model what to do next. Those repeated steps make end-to-end latency and resource use different from a single prompt-and-response benchmark. Context can grow during a task, tools can leave a model waiting, and several agents or requests may run at once.
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NVIDIA describes agentic inference as multi-turn work with variable sequence lengths, long contexts, pauses for tool calls, and bursts of sub-agent activity. Its guidance says a task may involve 50–500 sequential model invocations and characterizes agent contexts of 30,000–120,000 or more tokens as common. Those are NVIDIA’s descriptions of agentic workloads, not a universal count or requirement for every application.
The CPU remains involved even when inference runs on a GPU. It coordinates the loop, executes tools, manages application state, and moves data. Conversely, an agent with CPU-intensive tools can still spend too much time waiting for model inference. The bottleneck has to be measured across the whole task.
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CPU-only vs. GPU-accelerated servers for agentic AI workloads
| Consideration | CPU-only server | GPU-accelerated server |
|---|---|---|
| Best case to evaluate | The model fits available system memory and measured inference meets the service target, or orchestration and tools account for much of the work. | Model inference is a bottleneck, and accelerator compute and memory can support the model, context, and intended concurrency. |
| Work that still runs on CPUs | Model inference as well as the agent loop, tools, state, and data handling. | The agent loop, tools, state, and data handling; GPU acceleration does not remove this CPU work. |
| Primary sizing question | Can system memory and CPU performance handle the model and the target task? | Can accelerator memory hold weights, KV cache, activations, and runtime overhead at the target context and concurrency? |
| Cost or performance winner | No universal cutoff or cost winner is established; test the intended configuration. | No universal cutoff or cost winner is established; test the intended configuration. |
A 2025 arXiv preprint, Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective, profiled five selected workloads: Haystack RAG, Toolformer, ChemCrow, LangChain, and SWE-Agent. In the tested setups, tool processing on CPUs reached 90.6% of total latency. That maximum illustrates why tool and orchestration time matters; it is not a general share of latency for all agents. The paper also reports CPU dynamic energy reaching 44% at large batch sizes and latency improvements for its proposed optimizations relative to its stated baseline. Those findings are specific to the workloads and conditions it studied.
Do AI agents need a GPU?
No. An agent can run on a CPU server if the model, serving stack, and workload fit system memory and the measured inference and task performance meet the application’s requirements. A CPU-only configuration is also worth evaluating when tool execution and orchestration are substantial parts of the task.
A GPU is worth evaluating when model inference is the bottleneck and the accelerator has enough compute and memory for the model and its workload. The useful comparison is not “CPU or GPU” in isolation: a GPU server still needs CPU capacity for tools, coordination, and data handling, and the GPU’s benefit depends on how often inference limits the task.
How much GPU memory do you need for an AI agent?
There is no single memory figure for an agent. AWS guidance identifies model weights, activations, KV cache, and runtime overhead as parts of inference memory use. Memory sizing therefore depends not only on the model and its precision, but also on context length, concurrent requests, and the serving runtime. Leave headroom for those components rather than sizing from model weights alone.
Weights are only the starting point
AWS gives an example estimate of approximately 70 GB for the weights of a 70-billion-parameter model in FP8, before KV cache and other runtime needs. That is an example for the stated model size and precision, not a complete memory requirement or a recommendation for a particular server.
Context and concurrency increase KV-cache use
AWS’s example for a Mistral-7B grouped-query-attention configuration—8 KV heads, 32 layers, head dimension 128, and bf16—estimates the KV cache as follows. These figures apply to that configuration and the request counts shown.
| Request context | One request | Four concurrent requests |
|---|---|---|
| 1,000 tokens | Approximately 0.12 GB | Approximately 0.49 GB |
| 16,000 tokens | Approximately 1.95 GB | Approximately 7.81 GB |
The example shows why a memory estimate for one short request can be misleading for an agent that carries a longer context or serves several requests concurrently. Your actual cache use depends on the model configuration and serving setup.
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AWS’s inference sizing guidance lists these accelerator capacities for example instance families. Confirm current instance availability and specifications before making a deployment decision.
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| AWS instance family example | Accelerator example | Memory listed by AWS |
|---|---|---|
| g6 | L4 | 22 GB |
| g6e | L40S | 44 GB |
| g7e | RTX PRO 6000 Blackwell | 96 GB |
| p5 | H100 | 80 GB |
| p5en | H200 | 141 GB |
| p6-b200 | B200 | 180 GB |
| p6-b300 | B300 | 268 GB |
These are AWS instance examples, not a ranking or a guarantee that a particular model, context, and concurrency will fit. The relevant capacity is the usable memory in the actual deployment, with room for cache and runtime needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to benchmark the server for your agent
AWS advises defining the model architecture and parameter count, precision, input and output token lengths, concurrency, and latency objectives before sizing for throughput. Benchmark with the intended inference backend and production quantization, using traffic that resembles the agent’s real tasks.
- Fix the workload. Record the model, precision or quantization, representative input and output lengths, context growth, tool sequence, and expected concurrent tasks. Include the application’s latency objective.
- Check memory fit first. Estimate weights, KV cache at the target context and concurrency, activations, and runtime overhead for the actual model and serving configuration.
- Test the whole task. Use the intended agent framework, tools, inference backend, and hardware configuration. Include realistic tool waits and data movement rather than timing only a model call.
- Measure complementary metrics. Record time to first token (TTFT), generated tokens per second, end-to-end task latency, concurrent requests, and requests per second. AWS states that “No single metric is sufficient on its own”: high aggregate throughput does not guarantee acceptable TTFT, and a good TTFT does not by itself establish adequate throughput.
- Compare cost against useful work. Track server or instance configuration, region, software versions, prompt and output profile, concurrency, and cache state alongside the measurements. Where agents use different numbers of calls or tokens to finish tasks, calculate cost per successful task as well as cost per token.
Use the same task profile and success criteria for CPU and GPU tests. Report the results with their conditions; otherwise, differences in context, concurrency, quantization, cache state, or tool latency can make the comparison misleading.
What published GPU benchmarks can—and cannot—tell you
Published accelerator results can help identify configurations to test, but the examples below do not settle the CPU-versus-GPU decision for an agent. They use selected GPU configurations and workloads rather than a controlled comparison of CPU-only and GPU-accelerated servers running the same agent task.
AWS SageMaker examples
In an AWS Machine Learning Blog example accessed October 13, 2026, selected SageMaker GPU instances were compared for specified model workloads. For a 512-input/256-output chat workload, AWS reported 2,397 output tokens per second for g7.48xlarge as the highest throughput in that comparison, and an estimated $0.90 per million output tokens for g7.2xlarge as the lowest cost. For a 3,500-input/200-output RAG workload, it reported 816 output tokens per second for g7.48xlarge and an estimated $2.29 per million output tokens for g7.2xlarge. These are results for those AWS examples, not an agentic workload comparison or a CPU-versus-GPU result. AWS cautions that results vary with model, quantization, token lengths, concurrency, latency targets, and cost objectives.
Google Cloud and NVIDIA architecture example
Google Cloud and NVIDIA reported a GB200 NVL72 system with Dynamo at an 8K-input/1K-output profile: more than 6,000 total tokens per second per GPU in throughput-optimized configurations, and 10 ms inter-token latency in latency-optimized configurations. This is a vendor-reported result for a particular architecture and recipe, not an independent baseline or a CPU-only comparison.
Serving software claims need workload context
NVIDIA describes Dynamo features that include separating prefill and decode across GPU pools, managing shared KV cache across HBM, DRAM, and NVMe tiers, and routing requests by cache affinity. NVIDIA reports cache hit rates up to 97%, fleet GPU utilization improving from 40–55% to 75–85%, and 2–3 times as many concurrent sessions per GPU node. These are NVIDIA’s reported figures, not independently established or typical results. Whether such mechanisms help depends on the workload and deployment.
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How to make the decision
- Start with the task, not a model-size rule. Record the model and precision, context and output lengths, tool behavior, concurrency, and service targets.
- Find the limiting stage. Measure inference, orchestration, tool processing, and waiting across complete tasks. If inference dominates, test GPU acceleration; if tools and coordination dominate, also test whether more CPU capacity or faster tools improve the outcome.
- Verify memory before throughput. Ensure the chosen configuration can accommodate weights, KV cache, activations, and runtime overhead under the target context and concurrency.
- Compare successful work at the required service level. Look at latency, throughput, concurrency, and cost together. For an agent, cost per successfully completed task can reveal differences hidden by cost per token alone.
The evidence available does not establish a controlled CPU-only versus GPU-server winner using the same agent framework, model, tool latency, concurrency, and service targets. Nor does it establish a universal total-cost-of-ownership winner. A representative benchmark of the intended task is the sound basis for choosing.
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