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Evaluate an agentic-AI server refresh by measuring complete agent episodes—not by counting GPUs or comparing peak specifications. Define the model calls, tool use, concurrency and service-level objectives (SLOs); baseline the existing server; test candidate systems with identical software, model artifacts, traffic and cache conditions; then compare useful episodes completed, latency, availability, energy and total cost. GPU memory, CPU coordination, PCIe and NUMA topology, networking, storage, power and cooling must be sized as one system.
Start with the agent episode, not the server bill of materials
An agentic workload is a chain of decisions and actions. A single user request can trigger planning, retrieval, tool calls, validation, retries and several model invocations before the episode completes. Dell and Principled Technologies describe a single agent task spawning hundreds of inference calls, with GPU compute, CPU coordination and tool execution operating together. That behavior makes a conventional “requests per second” estimate inadequate.
Write a workload contract before looking at hardware. The contract should state:
- Episode boundary: the event that starts the task and the exact success condition that ends it.
- Model mix: model family, parameter size, precision or quantization, tokenizer and backend for every call.
- Context distribution: typical, p95 and worst-case prompt and retrieved-token lengths.
- Tool profile: tools used, average and p95 tool latency, payload sizes, parallel calls, retries and timeout behavior.
- Output profile: generated-token distribution, stop conditions and streaming requirements.
- Concurrency: simultaneous episodes, bursts, queue limits and expected growth.
- SLO: time to first token (TTFT), inter-token latency, request latency and total episode latency, along with availability and error targets.
Without these definitions, a server can hold the model weights yet miss the episode SLO when context length, tool fan-out or concurrent users increase.
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Separate request and episode SLOs
Record latency at both levels. A request-level result describes one model invocation; an episode-level result includes routing, every model call, tool execution, retries and orchestration overhead. Set a target for each so a fast individual response does not hide a slow multi-step task.
Measure the current server before specifying a replacement
Use the existing production-like path as the baseline. Include CPU-only execution or the installed GPU path, the same orchestration and the same external tools that a replacement must support. Capture enough detail to reproduce the run:
- Model files, tokenizer, quantization, backend, runtime and container image.
- Hardware profile, GPU count and type, CPU socket layout, memory size, PCIe topology, NICs and storage paths.
- Network mode, routing policy, parallelism, batching and admission control.
- Prompt and output distributions, concurrency schedule and tool-call mix.
- Cache state: cold start, partially populated cache and warm cache.
- Power, temperature, clock behavior, GPU and network health, failed placements and recovery events.
Keep the baseline artifacts and configuration under version control. A refresh comparison is valid only when the old and new systems run the same model artifacts, software versions, traffic profile and cache condition.
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- TTFT: time from request acceptance to the first generated token; sensitive to prompt processing, queueing and model-data movement.
- Inter-token latency: spacing between generated tokens during decode; a direct indicator of streaming smoothness.
- Request latency: end-to-end time for one model call, including queueing and routing.
- Episode latency: elapsed time from the initial user request to a successful agent result.
- Throughput: tokens, requests and completed episodes per second at a stated concurrency and SLO.
- Error rate: model, tool, timeout, placement and transport failures, separated by cause.
- Startup and recovery: download time, cache-hit time, disk-to-GPU transfer, peer transfer, first-ready time and restoration time after a failed process or node.
- Capacity headroom: sustainable utilization margin at the target duty cycle, not just the maximum reached during a short test.
Size GPU memory and compute for the whole call pattern
GPU selection starts with memory capacity and usable concurrency, then considers compute throughput. Weight memory is only one component. Account for activations, runtime workspace, context length, KV-cache policy, batching and any replicas or parallel shards. A model that loads successfully can still fail its latency SLO when concurrent episodes expand the KV cache.
NVIDIA’s certified-system guidance provides a useful starting point for inference, not a universal bill of materials:
| Item | Starting guidance | How to validate it |
|---|---|---|
| GPUs per server | Two, four or eight GPUs as balanced configurations | Test the model’s memory footprint, parallelism and target concurrency at each count. |
| CPU capacity | At least six physical CPU cores per GPU | Check tokenizer, scheduling, tool orchestration, data preparation and network-processing utilization. |
| System memory | At least twice the total GPU memory | Include model staging, CPU-side KV or activation buffers, page cache, tools and concurrent processes. |
| GPU placement | Balanced across CPU sockets and PCIe root ports | Verify bandwidth and latency for every GPU-to-CPU, GPU-to-NIC and GPU-to-storage path. |
| Local NVMe | At least 1 TB per CPU socket | Measure model load, cache-hit and cache-miss times under simultaneous startup. |
| Management and security | Redfish 1.0 or greater; TPM 2.0 secure boot | Confirm support in the server firmware, management controller and operating-system image. |
Treat each ratio as a screening threshold. Model size, precision, context distribution, batching, concurrency and whether prefill and decode are distributed can require more capacity. Conversely, a smaller deployment may be appropriate when the workload has low concurrency and relaxed SLOs.
CPU and memory are coordination resources
Agents spend CPU time outside the neural-network kernels: tokenization, prompt assembly, retrieval, JSON validation, tool clients, scheduling, encryption, logging and response streaming. Measure CPU saturation per socket and memory bandwidth, not only average utilization. A CPU bottleneck can leave GPUs idle while episode latency rises.
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Prefill and decode may need different treatment
Long prompts stress prefill compute and memory bandwidth; long generated answers stress decode and KV-cache capacity. If the serving stack separates those phases, test the network and synchronization cost of moving requests or KV data between them. If it does not, size for the phase that dominates your measured episode profile.
Check PCIe, NUMA, network and storage topology
Two servers with the same GPU count can deliver different results because their paths between devices differ. Draw the topology and test it rather than trusting a specification sheet.
PCIe and NUMA
- Place GPUs evenly across CPU sockets and PCIe root complexes.
- Prefer NICs and NVMe devices under the same PCIe switch or root complex as the GPUs they serve, where practical.
- Measure peer-to-peer GPU transfers, host-to-device copies and cross-socket traffic.
- Pin serving processes, memory allocation and interrupt handling so a GPU is not routinely fed through a remote NUMA node.
Networking
For a single-node deployment, high-speed inter-node networking is typically unnecessary, although the application and storage paths can still require substantial bandwidth. For multi-node inference, NVIDIA’s guidance starts at 200 Gbps and lists configurations up to 400 Gbps per GPU. These are starting points; validate delivered bandwidth, latency, congestion behavior and the RDMA path rather than relying on port labels.
Include network traffic generated by retrieval, tool services, model routing, distributed prefill or decode, checkpoint and cache transfers, telemetry and failure recovery. A fast adapter connected through an oversubscribed switch or a remote PCIe path may not provide the expected result.
Model storage and cache
Measure cold and warm paths separately. Record download time, checksum or unpack time, cache-hit time, disk-to-GPU transfer, GPU-to-GPU or node-to-node transfer and time until the endpoint is actually ready. Shared storage can simplify fleet management but introduce contention; local NVMe can shorten startup while requiring cache invalidation and capacity planning. Test simultaneous restarts because a rolling update can create a storage burst unlike steady-state serving.
Run a controlled refresh benchmark
NVIDIA’s inference reference architecture calls for retaining the model, tokenizer, backend, container image, hardware profile, network mode, storage path, prompt profile, output profile, concurrency and cache state for every workload study. Use the following staged procedure.
- Define representative episodes. Select the model family and size, context distribution, tool calls, retries, output length, concurrency and target SLO. Include normal and p95 scenarios.
- Establish the baseline. Run the current server through the production-like path. Record TTFT, inter-token latency, request and episode latency, throughput, errors, startup, recovery, power and thermal data.
- Search configurations. Vary GPU count, tensor or pipeline parallelism, backend, batching, queue limits and placement. Keep the workload and software artifacts constant while changing one factor at a time where possible.
- Benchmark endpoints under identical traffic. Use the same arrival pattern, concurrency ramp, prompts, tool responses, timeouts and success criteria on old and new systems. Report p50, p95 and p99 values, not only averages.
- Test cold and warm behavior. Empty the relevant caches for a cold run, then repeat with a warm cache. Measure download, storage, transfer and first-ready stages independently.
- Stress the target duty cycle. Run long enough to expose sustained-power and thermal effects. Record throttling, clock changes, error rate, recovery and rack-level limits.
- Test failures. Remove a worker, restart a serving process, interrupt a tool and exhaust a cache. Measure rerouting, episode success, data movement and time to restore capacity.
- Document the decision. Approve the refresh only when measured gains, power budget, supportability, operating procedures and rollback thresholds are written down.
Keep comparison criteria identical
Do not compare a warmed, quantized model on the new system with a cold, full-precision model on the old one. Keep precision, tokenizer, backend, container, prompt set, output limits, concurrency, cache state and success definition identical. If a change is necessary, run a separate controlled comparison and label it as such.
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Compare systems on useful agent capacity
Peak tokens per second is not the decision metric. Compare how many successful episodes the system completes while meeting the SLO and remaining inside its power and thermal envelope.
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|---|---|
| GPU memory and concurrency | How many concurrent episodes fit with the observed context and KV-cache distribution? What happens at p95 context length? |
| CPU cores and memory bandwidth | Can tokenization, orchestration, retrieval, logging and tool clients keep GPUs fed without cross-socket contention? |
| PCIe and NUMA topology | Are GPUs, NICs and NVMe balanced and connected through low-latency paths? |
| GPU interconnect | Does peer traffic sustain the selected parallelism without excessive synchronization delay? |
| NIC and RDMA path | What bandwidth and latency are delivered under load, and is the switch fabric oversubscribed? |
| Local NVMe and shared cache | How quickly do cold starts, rolling updates and recovery complete under concurrent access? |
| Software and orchestration | Are the backend, drivers, container runtime, scheduler, autoscaling and observability stack supported? |
| Remote management and security | Are Redfish controls, TPM 2.0 secure boot, firmware updates, audit logs and out-of-band recovery available? |
| Power and cooling | Can the rack, circuits, cooling system and facility distribution sustain the measured duty cycle with headroom? |
| Serviceability and lifecycle | Can failed GPUs, fans, drives or power supplies be replaced without unacceptable downtime, and is the support lifecycle suitable? |
| Availability headroom | What capacity remains after a node, GPU or network path fails? |
| Total cost per successful episode | What is the cost of hardware, software, power, cooling, space, operations and failures divided by completed episodes that meet the SLO? |
Power, cooling and rack density are hard capacity limits
Measure sustained power at the target duty cycle, not the nameplate alone. Include host CPUs, GPUs, memory, NICs, drives, fans, power-conversion losses and facility overhead. Check circuit limits, rack distribution, cooling-water or air capacity where applicable, inlet-temperature limits and the effect of neighboring racks. Leave headroom for bursts, firmware changes and degraded operation after a component failure.
Vendor efficiency figures can help identify candidates but are not universal guarantees. NVIDIA reported AgentPerf results in which GB300 NVL72 delivered up to 20 times more agents per megawatt than an H200 system in the cited workload. NVIDIA later reported vendor results of up to 15 times and 80 times the throughput per megawatt for GB300 NVL72 versus H200 NVL8 on two workloads, plus a preview claim of up to 30 times for Vera Rubin over GB300. Normalize model, precision, backend, concurrency, SLO, cooling and facility overhead before using any such result in a business case.
Report your own energy metric as kilowatt-hours per successful episode and episodes per megawatt at a stated SLO. A system that consumes less power but misses the episode target is not more efficient for the application.
Evaluate on-premises refresh versus cloud capacity
Use the same episode metric for both choices. For on-premises hardware, include purchase or lease cost, deployment, rack space, power, cooling, networking, software support, staffing, spare parts, maintenance and the cost of unused capacity. For cloud capacity, include instance, attached storage, data transfer, managed-service, egress, idle and autoscaling charges, plus any required dedicated networking.
Cloud capacity can reduce procurement time and make burst testing easier, but sustained high utilization may make dedicated equipment more economical. On-premises systems can provide predictable placement and data locality, but the organization owns capacity risk, hardware failures and lifecycle planning. Compare at the concurrency distribution you actually expect, including quiet periods and seasonal peaks.
Use a break-even model
- Estimate successful episodes per month at the target SLO, not raw requests.
- Calculate fully loaded monthly on-premises cost, including amortization or lease, facilities and operations.
- Calculate cloud cost for the same episode volume, separating steady-state and burst capacity.
- Add the cost of availability headroom: redundant nodes, reserved cloud capacity or both.
- Run sensitivity cases for model growth, context expansion, utilization, energy price and failure rate.
Do not claim a cheaper option from list prices alone. The available evidence does not establish retail pricing, regional inventory, warranty comparisons or procurement lead times; obtain current quotations and support terms for the deployment region.
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Hardware examples require reproduction on your workload
Dell reports testing PowerEdge XE7740 configurations with Intel Xeon 6747P processors and NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on two agentic-AI workloads to provide sizing guidance. Treat those results as platform-specific evidence, not a guarantee for another model, context profile or orchestration stack. Reproduce the test with your model artifacts, traffic and SLO before selecting the configuration.
NVIDIA Enterprise Reference Architectures describe validated patterns spanning compute, networking, storage, software, orchestration and observability. The RTX PRO AI Factory profile is aimed at small and medium model inference, while the HGX profile targets large GPU nodes and multi-node large-language-model workloads. These profiles can narrow the design space, but they do not remove the need to verify topology, facility limits and measured episode performance.
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A refresh should have explicit go/no-go gates before production migration:
- Episode p95 and p99 latency meet the SLO at planned concurrency.
- Successful-episode throughput improves by the required amount with no unacceptable error increase.
- Cold-start and recovery times fit deployment and incident objectives.
- Sustained power, temperature and rack limits remain below documented ceilings with headroom.
- Security, firmware, driver, container and orchestration support are validated.
- A degraded mode still meets the minimum service after a planned failure.
- Rollback preserves the old endpoint, model artifacts and routing configuration.
During a phased rollout, route a controlled percentage of episodes to the new system and compare identical cohorts. Roll back when episode SLO violations, error rates, thermal throttling or recovery times cross the pre-approved thresholds; do not wait for a capacity outage.
Common refresh mistakes and fixes
Choosing by GPU count
Symptom: a larger GPU count produces little episode improvement. Fix: inspect memory pressure, CPU utilization, PCIe placement, synchronization and tool latency; the bottleneck may not be GPU arithmetic.
Benchmarking only warm steady state
Symptom: normal traffic is fast but deployments or failures cause long outages. Fix: measure cold start, cache misses, simultaneous restarts, model movement and recovery as separate scenarios.
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Ignoring context and retries
Symptom: a prototype passes with short prompts but queues under production traffic. Fix: use the full context distribution, tool fan-out, retry policy and p95 output length in the load profile.
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Relying on port labels
Symptom: a network advertised as 200 or 400 Gbps does not deliver expected distributed-inference performance. Fix: test end-to-end bandwidth and latency through the actual NIC, PCIe path, switch fabric and RDMA configuration.
Leaving no facility headroom
Symptom: throttling or circuit limits appear during sustained load. Fix: test at the target duty cycle, include facility overhead and retain capacity for bursts and degraded operation.
Server-refresh decision checklist
- Is the agent episode and success condition written down?
- Are model calls, tools, retries, context, outputs, concurrency and SLOs quantified?
- Does the baseline include CPU, GPU, network, storage, startup, recovery, power and thermal data?
- Were old and new systems tested with identical artifacts, software, traffic and cache states?
- Does GPU memory fit the model, context and KV-cache policy at p95 concurrency?
- Are CPU cores, memory bandwidth, PCIe, NUMA, GPU interconnect, NICs and NVMe balanced?
- Were cold-start, warm-cache, sustained-duty-cycle and failure scenarios measured?
- Is delivered network performance validated rather than inferred from link speed?
- Are rack power, cooling, density, serviceability and lifecycle documented?
- Is total cost calculated per successful episode with availability headroom?
- Are rollout, rollback and failure thresholds approved before migration?
Frequently Asked Questions
How often should an agentic-AI server benchmark be repeated?
Repeat the controlled benchmark whenever the model, quantization, serving backend, driver, container, orchestration policy, context distribution or hardware topology changes materially. Re-run a smaller health benchmark after routine firmware or software updates.
What evidence should be retained for an infrastructure review?
Retain the workload definition, exact model and tokenizer hashes, container and driver versions, hardware and topology diagrams, traffic schedule, cache state, latency distributions, throughput, errors, power, thermal data, startup and recovery results, and the approved rollback thresholds.
Can a server pass an inference benchmark and still fail in production?
Yes. Production may have longer contexts, more tool fan-out, retries, cache misses, noisy neighbors or a different arrival pattern. Validate representative episodes and include cold-start, failure and sustained-duty-cycle tests before migration.
What is the most useful single efficiency metric?
Use kilowatt-hours per successful episode at the stated SLO, paired with episodes per megawatt. This ties energy to work that users actually receive instead of to raw token throughput.
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