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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 objective (SLO); benchmark the existing server and candidate systems with identical software and traffic; then compare useful episodes per second, latency, errors, availability, energy and total cost per successful episode. A refresh is justified only when the measured gain fits your power, cooling, support and rollback limits.
Why agentic workloads change server sizing
A conventional inference test often measures one prompt and one response. An agentic application runs an episode: a user request is interpreted, a model plans, tools are called, results are inserted into context, and the model may reason and act repeatedly until it reaches an outcome. Retries, safety checks, routing and human approval can add more calls. Dell and Principled Technologies describe the consequence this way: "A single agent task can spawn hundreds of inference calls, each requiring GPU compute, CPU coordination, and tool execution working in tight concert."
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That chain makes a server that looks fast in a single-request benchmark fail in production. GPU memory can limit concurrent contexts even when model weights fit. CPU threads can become the bottleneck while GPUs wait for tokenization, tool execution or scheduling. PCIe placement can constrain transfers, and a shared storage or network path can delay model loading. The correct unit for planning is the completed episode at its required latency and reliability, not tokens per second from an isolated kernel.
Define the episode and SLO before choosing hardware
Write a workload specification that another team can reproduce. Record the following values for each important agent workflow:
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- Model: family, parameter size, precision or quantization, tokenizer and serving backend.
- Context: input-token distribution, retrieved-document size, system-prompt size and maximum context length.
- Actions: tool types, average and percentile tool-call count, serial versus parallel calls, external-service latency and retry rules.
- Generation: output-token distribution, stop conditions, structured-output requirements and sampling settings.
- Traffic: requests per second, simultaneous episodes, burst size, arrival pattern and priority classes.
- Episode SLO: target completion latency, time-to-first-token (TTFT), inter-token latency, success rate and recovery objective.
- State: expected cache hit rate, conversation history, KV-cache policy and whether prefill and decode run on the same or different devices.
Use representative distributions rather than a single average. A 95th- or 99th-percentile context can determine GPU-memory pressure, while a long-tail tool call can dominate episode latency. Define what counts as success: for example, an episode that returns a syntactically valid answer after a tool timeout may be a failure for one application and a degraded success for another.
Measure the behavior that users experience
Collect metrics at request, model-call and episode levels. Keep the boundaries clear so a fast model call does not hide a slow tool or scheduler.
| Metric | What it reveals | How to use it |
|---|---|---|
| Time to first token | Queueing, prompt prefill, routing and startup delay | Track median and tail values for interactive SLOs. |
| Inter-token latency | Decode responsiveness after generation begins | Measure by output length and concurrency; a good average can hide stalls. |
| Request latency | Time for one model call, including queueing | Correlate with batch size, sequence length and GPU utilization. |
| Episode latency | End-to-end time through model calls, tools and retries | Use as the primary user-facing SLO. |
| Throughput | Completed model calls and completed episodes over time | Report useful successful episodes, not only generated tokens. |
| Concurrency and queue depth | How load translates into waiting | Find the point where latency or error rates breach the SLO. |
| Error and retry rate | Capacity, placement, timeout and dependency failures | Count failed episodes separately from retried model calls. |
| Startup and recovery time | Cold-start and failure impact | Record model download, cache restore, process restart and node recovery. |
Capture GPU memory occupancy, allocator failures, CPU utilization and run-queue depth, PCIe transfer rates, GPU-to-GPU traffic, NIC throughput and latency, storage throughput, power draw, temperature and throttling. Record routing decisions and failed placements so a scheduler issue is not mistaken for insufficient compute.
Translate model requirements into a balanced server
GPU memory is a concurrency budget
Model weights are only one allocation. Activations, runtime workspaces, tokenizer or embedding services and the KV cache consume additional memory. Longer contexts and more simultaneous episodes increase KV-cache use; batching can improve throughput while raising the memory floor. Quantization reduces weight storage but can change quality, kernel availability and decode speed. Test the exact precision and backend intended for production.
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Separate a "model fits" test from an SLO test. A server may load the model successfully yet fail when concurrent agents expand context or issue parallel tool calls. Report the maximum concurrency that meets the episode SLO with headroom for bursts and one degraded device or process.
CPU coordination and system memory
NVIDIA’s Certified Systems Configuration Guide uses at least six physical CPU cores per GPU as a starting point for inference systems and recommends system memory of at least twice the aggregate GPU memory. These are balancing guidelines, not guarantees. Tokenization, HTTP handling, tool execution, retrieval, serialization and orchestration may require more CPU, while a simple, highly optimized service may require less. Count physical cores, memory bandwidth and NUMA locality, then verify with the actual agent runtime.
PCIe, NUMA and placement
Place GPUs evenly across CPU sockets and PCIe root ports. Attach NICs and NVMe devices to the same PCIe switch or root complex as the GPUs they serve where possible, reducing cross-socket traffic. Confirm the topology with the operating system and vendor diagrams rather than assuming that adjacent slots share a switch. Measure peer-to-peer GPU transfers, host-to-device copies and remote-NUMA accesses under load.
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For multi-node inference, NVIDIA’s guidance starts at 200 Gbps and its configuration table reaches up to 400 Gbps per GPU. Treat those figures as design points: validate delivered bandwidth, latency, congestion behavior and the RDMA path with your message-passing and serving stack. Port speed printed on a NIC is not proof that the application receives that bandwidth.
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Single-node deployments generally do not need high-speed networking between multiple AI nodes, but they may need fast links to application services, retrieval databases, object storage or users. NVIDIA states: "Single node deployments typically do not require high-speed networking to connect multiple nodes for your AI workload but may require it for connecting to other applications." Measure those application and storage paths even when the model stays inside one chassis.
Local NVMe, shared storage and cache
The same NVIDIA guidance recommends at least 1 TB of local NVMe per CPU socket as a starting point. Size beyond that when several model variants, container layers, tokenizer files or retrieval indexes must coexist. Test cold starts separately from warm-cache runs: record download time, shared-storage read time, disk-to-GPU transfer, peer transfer and the time until the endpoint is genuinely ready. A fast GPU cannot compensate for repeated multi-gigabyte loads from an oversubscribed file system.
Management and security prerequisites
For remotely operated servers, require Redfish 1.0 or newer for inventory, power control and health automation, and TPM 2.0 with secure boot for measured startup and key protection. Confirm firmware update procedures, GPU telemetry, remote console access and audit logging before deployment; these capabilities affect recovery time as much as raw hardware.
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When more CPU and RAM beats another GPU
Profile the episode before buying accelerators. More GPUs are appropriate when GPU utilization, memory capacity or decode throughput is the limiting resource and CPU, network and storage remain below their limits. More CPU or RAM is preferable when GPUs are idle while tokenization, retrieval, tool execution, serialization or scheduling queues grow. A workload with modest models but hundreds of short tool-mediated calls may be coordination-bound. Conversely, long-context generation at high concurrency is usually memory- and GPU-bound.
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- Choose additional GPU memory when out-of-memory errors or KV-cache eviction limit concurrency.
- Choose faster or more numerous GPUs when decode time breaches the SLO after the model is resident and the host is not saturated.
- Choose more CPU cores or memory bandwidth when CPU run queues, tokenization or orchestration delay GPU launches.
- Choose better PCIe or NUMA balance when remote-memory traffic and host-device copies dominate.
- Choose faster storage or networking when cold starts, retrieval or inter-node transfers dominate episode time.
A reproducible refresh benchmark
Use the following staged process for the current server and every candidate. Keep the test harness and acceptance criteria unchanged.
- Define representative episodes. Select model family and size, precision, context distribution, tool calls, retries, output lengths, concurrency levels and target SLOs. Include the most important and the most demanding workflows.
- Baseline the current server. Run the production CPU-only or existing-GPU path, recording all metrics and health counters. Save configuration, firmware, driver, container and orchestration versions.
- Freeze the artifacts. Retain the exact model files, tokenizer, backend, container image, hardware profile, network mode, storage path, prompt and output profiles, concurrency and cache state. NVIDIA’s inference reference architecture calls for these details to remain with each workload study.
- Search configurations. Vary GPU count, tensor or pipeline parallelism, batching, backend settings and placement. Keep traffic identical and reject configurations that meet throughput only by violating the episode SLO.
- Run endpoint tests. Measure TTFT, inter-token latency, request latency, episode latency, throughput, concurrency, errors and routing behavior at steady state and during bursts.
- Separate cold and warm operation. Measure model download, cache hit and miss, disk-to-GPU transfer, peer transfer and first-ready time. Report warm-cache capacity separately from restart and scale-out behavior.
- Stress the duty cycle. Sustain the target load long enough to expose power or thermal throttling. Record temperatures, clocks, power, error rate, recovery time and rack-level limits.
- Test failure and recovery. Remove a worker, restart a model process, exhaust a cache and interrupt a network or storage path where safe. Measure failed placements, rerouting, degraded capacity and time to restore the SLO.
- Document the decision. Approve the refresh only when measured gains, power and cooling requirements, supportability, availability headroom and rollback thresholds are written down.
Compare the old and new systems on the same axes
| Comparison area | Questions to answer |
|---|---|
| GPU memory and usable concurrency | How many episodes meet the SLO before KV-cache pressure or out-of-memory errors? |
| GPU compute and interconnect | What are prefill and decode rates at the target precision, and how do peer transfers behave? |
| CPU cores and memory bandwidth | Can the host tokenize, retrieve, execute tools and schedule without queue growth? |
| PCIe and NUMA topology | Are GPUs, NICs and NVMe balanced across sockets and root complexes? |
| Network and RDMA | What bandwidth and latency reach the application under contention? |
| Storage and cache | How long do cold starts take, and what happens after a cache miss or node restart? |
| Software and orchestration | Are the model backend, drivers, containers, scheduler and monitoring stack supported? |
| Observability and management | Can operators identify a failed placement, throttled GPU, bad link or unhealthy disk remotely? |
| Security | Are secure boot, TPM, firmware controls, isolation and audit requirements met? |
| Power and cooling | Does the rack deliver sustained power and airflow without throttling or violating facility limits? |
| Serviceability and lifecycle | Can components be replaced within the recovery objective, and will support last for the planned life? |
| Economics | What is the total cost per successful episode at the required availability and duty cycle? |
Use the same model artifacts, prompt mix, cache state and success definition in both systems. If a candidate uses a different backend or precision, treat it as a separate configuration and disclose the quality and operational differences rather than presenting the speedup as hardware-only.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Power, cooling and rack capacity are hard limits
Measure sustained—not momentary—power at the target duty cycle. Include CPU, GPUs, memory, NICs, storage, fans, power-conversion losses and facility overhead. Check rack power distribution, breaker capacity, cooling-water or air requirements, inlet temperature and neighboring equipment. Leave headroom for traffic bursts, firmware behavior and a failed node; a system that reaches its thermal limit during a benchmark will deliver less useful throughput in production.
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How to interpret vendor and platform examples
RTX PRO 6000 Blackwell Server Edition and PowerEdge XE7740
Dell reports testing PowerEdge XE7740 configurations using Intel Xeon 6747P processors and NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on two agentic-AI workloads to provide sizing guidance. Treat that result as platform-specific evidence. Reproduce it with your model, context distribution, tool latency, concurrency, software stack and SLO before applying the result to another chassis or region.
Reference architectures
NVIDIA Enterprise Reference Architectures describe validated patterns spanning compute, networking, storage, software, orchestration and observability. NVIDIA identifies an RTX PRO AI Factory profile for small and medium-model inference and an HGX profile for large GPU nodes and multi-node large-language-model workloads. These profiles narrow design risk, but they do not replace a workload benchmark or confirm your facility’s power, cooling and support constraints.
Vendor-reported efficiency claims
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 higher 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. These are vendor-reported, workload-specific figures—not universal guarantees. Before using one in a business case, normalize model, precision, backend, concurrency, SLO, cooling method and facility overhead, then rerun the comparison on your traffic.
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No current official retail prices, regional inventory, warranty terms, procurement lead times or current program eligibility were published, so a universal cheaper-or-more-reliable verdict is not supportable. Build a comparable cost model instead.
- On-premises: include server, networking, storage, rack and power upgrades, cooling, software support, staffing, spares, depreciation and the cost of unused capacity. Credit predictable latency, data locality and control only when your operations team can maintain them.
- Cloud: include accelerator rental, attached storage, data transfer, inter-region traffic, managed-service fees, startup time, minimum commitments and egress. Measure capacity availability and cold-start behavior in the region and account for rate changes.
- Both: calculate cost per successful episode at the same SLO, quality level, concurrency, availability target and energy boundary. Include retry work, idle failover capacity and migration or exit costs.
A hybrid or staged approach can preserve a rollback path: keep the existing server for a defined period, burst overflow to an approved capacity pool, and migrate only after the new path meets the acceptance gates.
Set acceptance gates and rollback thresholds
Write thresholds before seeing results. Examples include maximum episode latency at the 95th and 99th percentiles, minimum successful episodes per second, maximum error and retry rates, cold-start and recovery limits, sustained rack power, inlet temperature and minimum spare capacity after one worker is unavailable. Define who can stop a rollout and how traffic returns to the old server.
During a phased deployment, compare old and new paths with the same traffic slice and cache conditions. Roll back when any critical SLO, quality check, thermal limit or recovery objective is breached; do not wait for an average metric to hide a tail failure. Keep model artifacts, configuration and telemetry from every phase so an apparent regression can be reproduced.
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Refresh decision checklist
- Episode definitions include model calls, tools, retries, context and output distributions.
- Concurrency and SLO targets are explicit, including tail latency and success criteria.
- Baseline and candidate use identical artifacts, traffic, cache state and comparison metrics.
- GPU memory, CPU cores, system RAM, PCIe, NUMA, NIC and NVMe topology are documented.
- Cold-start, warm-cache, sustained-power, throttling, failure and recovery tests are complete.
- Multi-node designs validate at least the required network bandwidth and latency rather than relying on port labels.
- Rack power, cooling, density and failover headroom are approved by facilities teams.
- Management, secure boot, TPM, telemetry, orchestration and lifecycle support are verified.
- Results are expressed as useful successful episodes, availability, energy and total cost per episode.
- Rollout, rollback and capacity-reservation thresholds are signed off before production traffic moves.
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