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Size an agentic AI server for the entire workload, not just model generation. The model may run on GPUs, while the host CPU and RAM handle orchestration, retrieval, tools, and sandboxes; storage may hold models, indexes, logs, and cached state. NVIDIA’s inference-server guidance is a useful starting configuration, not a universal rule or a guarantee of production performance.
What changes the size of an agentic AI server?
An agentic workload combines model inference with the software that decides what to do next. Between model calls, an agent may route requests, retrieve data, run tools, update state, or launch sub-agents. Those activities can stress different parts of the server, and the bottleneck can shift during a session.
Before choosing hardware, define the workload you need to serve:
- Inference location: Will the model run on this server, on a CPU-only host, or through a remote API? A host calling a remote model still needs resources for its own orchestration, retrieval, tools, and any local sandboxes.
- Model and serving configuration: Record the model family and size, precision, GPU type and count, and expected runtime overhead.
- Context and output: Estimate prompt and response token lengths, including how much conversation or agent state must remain available.
- Concurrency: Count simultaneous sessions and requests, plus concurrent sub-agents or branches where possible. A single user can create substantial parallel work.
- Workflow components: Identify retrieval-augmented generation (RAG), vector search, tool processes, sandboxes, persistent state, and any data that must stay local.
- Service targets: Set the throughput, time to first token, inter-token latency, availability, and task-completion targets the system must meet.
Lenovo’s LLM Sizing Tool exposes inputs including model, precision, input and output tokens, concurrent users, requests per user per minute, GPU overhead, RAG, high availability, and GPU count. It also supports CPU-only sizing. Lenovo says its estimates are guidance for capacity planning, not guaranteed production performance.
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What baseline does NVIDIA recommend for GPU inference?
NVIDIA’s Certified Systems Configuration Guide, updated September 30, 2026, gives the following recommendations for its described inference-server configuration. Treat them as a vendor baseline for planning a GPU inference server—not as a minimum for every agent host, model, or deployment.
| Component | NVIDIA recommendation | What it means for planning |
|---|---|---|
| CPU sockets | At least 2 | Check that the selected server platform and its topology support the GPU, memory, and expansion-card layout you need. |
| CPU capability | PCIe Gen5-capable CPUs, or later when available; at least 2.0 GHz base clock | Confirm supported interface generation and the actual server’s lane and slot layout. |
| CPU cores | At least 6 physical CPU cores per GPU | This is NVIDIA’s inference-server recommendation, not a universal core-to-GPU law for agent workloads. |
| System memory | At least 2× the total GPU memory | This is a system-RAM baseline in the guide. Workload-specific host processes or cache needs may require a different capacity. |
| Local storage | 1 NVMe drive per CPU socket; at least 1 TB | Confirm the server supports the drive’s form factor and interface, and size total capacity for your data and state needs. |
| Multi-node networking | At least 200 Gbps; up to 400 Gbps per GPU | Applies to the guide’s multi-node inference recommendation, not every single-server deployment. |
The guide also recommends balancing GPUs across CPU sockets and PCIe root ports. Where appropriate, place network adapters and NVMe drives within the same PCIe switch or root complex as GPUs. Its guidance says: “Specific use cases for your application workload should be discussed with your integration partner.”
How should you plan GPU memory, host RAM, and CPU cores?
Plan GPU memory for model execution and active context
GPU memory is a separate budget from system RAM. Plan it for model weights and inference-runtime overhead, then account for the active context and serving configuration. More concurrent sessions or longer retained contexts can increase memory pressure; a system that fits the model weights may still run short of capacity at the target concurrency.
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NVIDIA describes a 128K-token context for a 70B model as requiring approximately 16–32 GB of KV cache. That is NVIDIA’s estimate, and actual cache use depends on model and runtime details. Treat it as an example of why context length belongs in the sizing assumptions, not as a universal cache figure.
Budget host RAM for the work around inference
System RAM supports host-side orchestration, retrieval, vector databases, tool processes, sandboxes, and possibly cache tiers. The NVIDIA 2×-GPU-memory recommendation is a useful initial reference for its inference-server configuration, but it does not establish a universal RAM-per-agent requirement.
A 2026 preprint, “From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems,” found different resource affinities across the applications it studied: some work was GPU-bound, retrieval could be memory-bound, and sandboxes could be CPU-bound. Non-LLM components dominated latency in five of the ten applications studied. Its peak observed sandbox working set was 28 GB per session; that is a study-specific observation, not a general per-session allocation rule.
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Size CPU capacity for orchestration and tools
Model generation may be GPU-heavy, but CPU work does not stop between model calls. Agent harnesses coordinate routing, tools, data access, and state. Sandboxes and concurrent tool executions can add CPU demand independently of GPU utilization. The six-physical-cores-per-GPU figure in NVIDIA’s guide gives a starting point for its stated GPU-server configuration; the right CPU count for your deployment depends on what runs on the host and how much runs in parallel.
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How much storage does an AI inference server need?
For its inference-server configuration, NVIDIA recommends one local NVMe drive per CPU socket and at least 1 TB of local storage. That is a vendor recommendation, not proof that 1 TB will hold every deployment’s models, logs, datasets, indexes, and cache.
Plan capacity around the data the server will actually keep:
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- Model files and versions deployed locally.
- Retrieval data and indexes, if they reside on the server.
- Logs and other persistent agent state, including retention requirements.
- Any KV-cache data deliberately moved out of GPU memory.
NVIDIA describes a possible multi-tier KV-cache design spanning GPU HBM, CPU DRAM, and NVMe when cache capacity exceeds faster memory. This can expand the amount of state kept available, but it also moves data between tiers; the capacity and latency trade-off depends on the serving stack and cache policy. NVIDIA does not give a universal NVMe capacity for all such deployments.
Before selecting drives, verify the server’s supported form factor and interface, available slots, capacity, and write-endurance needs for the expected workload. A drive’s nominal capacity alone does not tell you whether it fits the platform or the cache design.
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Component totals are not enough if the platform cannot connect them effectively. Check the server’s CPU-socket layout, memory channels, PCIe lanes and generations, root ports, GPU interconnect, network adapters, and NVMe slots. Confirm that the chosen configuration also meets airflow and power requirements.
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For a multi-node inference deployment, NVIDIA’s guide recommends a network adapter of at least 200 Gbps and up to 400 Gbps per GPU. That recommendation is specific to multi-node inference; a single-server workload should be sized for its own data and traffic patterns rather than inheriting that network figure.
How can you compare server options and validate the design?
Compare complete configurations against the same representative workload. A useful comparison includes:
- Workload capacity: Supported models and precision, target context length, concurrent sessions, and sub-agent activity.
- Compute and memory: GPU count and memory, physical CPU cores, system RAM capacity and bandwidth, and the fit between CPU resources and host-side work.
- Data movement: PCIe lanes and generation, socket and root-port balance, GPU interconnect, network capacity, and local versus remote data access.
- Storage and state: NVMe capacity and interface, endurance, model and index storage, logs, and whether cache offload is implemented.
- Service behavior: Throughput, time to first token, inter-token latency, task completion, availability, and cost under the same workload.
Use sizing tools to form an initial estimate, then validate with a representative run. Lenovo’s tool reports estimates such as throughput, latency, and maximum concurrent users, but its outputs are planning guidance rather than guaranteed results. Measure the whole workflow, including tools and retrieval, rather than looking only at model-generation speed.
Micron’s 2026 white paper illustrates why setup details matter. In its single-user CrewAI speechwriter workflow experiment using Meta-Llama-3.1-8B-Instruct-FP8, the described system had 192 GB of HBM3e and 2 TB of DDR5 host memory. Micron reported GPU-utilization spikes of up to 83% during LLM interactions. In a separate scaling evaluation on that setup, inference time rose to about 8.2× at 1,000 requests and 105.6× at 10,000 requests versus the 100-request baseline; the KV-cache budget was fully used at the 10,000-request batch size. These results belong to Micron’s specified experiment and analysis, not a prediction for another server.
- Write down the target workload. Record inference location, model and precision, input and output lengths, expected concurrent sessions and branches, and any RAG, tools, or sandboxes.
- Estimate each resource pool separately. Plan GPU memory for model execution and active context; plan host RAM and CPU for orchestration, retrieval, tools, and sandbox activity; plan storage for persistent data and any cache tier.
- Map the components to the platform. Check sockets, cores, memory channels, PCIe layout, GPU balance, NVMe slots, network capacity, power, and cooling.
- Test at representative concurrency. Track throughput, time to first token, inter-token latency, task completion, and the point at which resource pressure appears.
- Resize around the observed bottleneck. If performance or concurrency misses the target, determine whether the constraint is GPU memory, host CPU or RAM, storage, or data movement before adding capacity.
The result should be a workload-specific configuration, not a single core-to-GPU ratio or RAM-per-agent number. The cited vendor baseline can anchor a GPU inference build, while measurements of the complete agent workflow determine whether that build meets your service targets.
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