There is no universal winner between Dell PowerEdge, HPE ProLiant and Lenovo ThinkSystem for AI. The right choice is determined by the accelerator generation, GPU memory, PCIe or SXM form factor, GPU-to-GPU interconnect, host memory, power delivery, cooling and management model. Dell is compelling when a documented H100/H200 portfolio and published benchmark evidence matter. HPE is strong when you need accelerator-focused chassis choices, iLO-based operations and air or direct-liquid cooling. Lenovo is attractive when you want EPYC or Xeon flexibility, dense four- or eight-GPU systems, or HGX H100/H200 configurations.
The comparison below treats unlike systems honestly. A Dell server with eight H100 SXM5 GPUs cannot be fairly ranked against an HPE server with eight older A100 SXM4 GPUs simply because both contain eight accelerators. Request a workload-matched test before committing to a purchase.
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Quick comparison
| Platform family | Representative AI systems | GPU capacity described by the vendor | CPU and memory highlights | Cooling and management |
|---|---|---|---|---|
| Dell PowerEdge | XE9680, XE8640, R760xa, XE7745 | XE9680: up to eight NVIDIA H100 SXM5; XE8640: four H100 SXM; R760xa: four H100 PCIe; XE7745: eight H200 in the cited configuration | XE9680 uses two fourth-generation Intel Xeon Scalable processors; other CPU core counts and maximum system-memory figures are configuration-specific and not stated here | PowerEdge management stack; chassis and GPU choice determine power and cooling requirements |
| HPE ProLiant Compute | DL380a Gen12, XD685 | DL380a Gen12: up to ten double-wide GPUs through PCIe 5.0; XD685: eight GPUs, with H200 and AMD Instinct MI300X/MI325X options | DL380a Gen12 uses Intel Xeon 6 processors; exact memory and CPU counts vary by build | XD685 is available air-cooled or direct-liquid-cooled; iLO 6, BMC and Redfish |
| Lenovo ThinkSystem | SR675 V3, SR680a V3 | SR675 V3: up to eight 600 W PCIe GPUs or a four-GPU HGX H100/H200 complex; SR680a V3: eight H100 or H200 SXM5 GPUs | SR675 V3 uses AMD EPYC and supports up to 6 TB DDR5; redundant power supplies up to 2,600 W. SR680a V3 uses two fifth-generation Intel Xeon Scalable processors | SR675 V3 uses XClarity Controller 2 and related XClarity software; SR680a V3 is an 8U platform with GPU-specific power and cooling requirements |
“Up to” figures describe supported configurations, not a promise that every SKU, regional model or power option can be combined. Confirm the exact bill of materials, firmware level and electrical requirements with the vendor.
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Why GPU architecture matters more than the server badge
AI performance is usually constrained first by the accelerator subsystem, not by whether the chassis carries a Dell, HPE or Lenovo logo. Four decisions dominate:
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- GPU architecture and memory: H100, H200 and AMD Instinct options have different memory capacities, bandwidth characteristics, software support and power envelopes.
- Form factor: PCIe cards plug into conventional expansion slots. SXM modules are installed in an engineered GPU baseboard and are intended for tighter accelerator coupling.
- Interconnect: Training jobs that exchange parameters frequently can benefit from high-bandwidth GPU-to-GPU paths and a suitable network fabric. A server with more GPUs can still underperform if communication becomes the bottleneck.
- Power and thermal design: High-wattage accelerators require appropriately sized power supplies, airflow or liquid loops, and datacenter capacity.
Compare systems at the level of the complete GPU complex: model, memory, interconnect, host CPU, system RAM, network adapters, software stack and cooling. Counting GPUs alone hides the differences that determine time to train, inference throughput and operational cost.
H100 PCIe, H100 SXM5 and H200: choosing the accelerator layout
H100 PCIe
PCIe H100 cards are suited to servers designed around standard expansion slots. The Dell R760xa is listed with four H100 PCIe GPUs, while Lenovo’s SR675 V3 can be configured with up to four or eight double-wide PCIe GPUs rated up to 600 W. PCIe designs can simplify integration with existing server layouts, but the number of cards, slot spacing, auxiliary power connectors and airflow must be checked together.
H100 SXM5
SXM5 systems use a purpose-built accelerator baseboard rather than ordinary PCIe slots. Dell’s XE9680 is a 6U system with up to eight H100 SXM5 GPUs. Lenovo’s SR675 V3 has a four-GPU HGX H100 option, and the SR680a V3 supports eight H100 SXM5 GPUs in an 8U chassis. These systems are intended for tightly coupled multi-GPU work, but they require a matching chassis, power design, firmware and cooling configuration.
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H200
H200 is available in the compared product families, including Dell’s eight-GPU XE7745 configuration, HPE’s XD685 options and Lenovo’s HGX and SXM5 systems. Dell reports up to 31,000 tokens per second for a Llama 2 workload in MLPerf Datacenter Inference on an XE7745 configuration. That is a vendor-reported result for a specific model, software stack and test condition; it should not be treated as a general H200 guarantee.
AMD Instinct alternatives
HPE lists AMD Instinct MI300X and MI325X options for the XD685. An AMD configuration can be sensible when your framework, libraries, model portability requirements and support organization are prepared for that software stack. Do not compare an AMD system and an NVIDIA system solely by GPU count or nominal power. Reproduce the intended model, precision, batch size and communication pattern on each platform.
Dell PowerEdge options
PowerEdge XE9680
The XE9680 is Dell’s 6U AI-focused platform with up to eight H100 SXM5 GPUs and two fourth-generation Intel Xeon Scalable processors in the cited configuration. Its value is density and a documented eight-GPU SXM design for large training or inference jobs. The SXM layout means the server must be evaluated as a complete appliance: GPU baseboard, host CPUs, memory population, network adapters, power supplies and cooling are interdependent.
PowerEdge XE8640 and R760xa
The XE8640 uses four H100 SXM GPUs. The R760xa uses four H100 PCIe GPUs, making it a different expansion and cooling proposition even though both systems are in the same accelerator generation. Choose between them according to the required GPU-to-GPU topology, slot-based expansion needs and rack power plan rather than chassis name alone.
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Dell reports an XE7745 configuration with eight H200 GPUs and up to 31,000 tokens per second for a Llama 2 workload in MLPerf Datacenter Inference. Ask for the exact model size, precision, batch, software versions and networking used in any proposal so your own throughput target can be compared on equal terms.
Where Dell fits best
- A documented H100/H200 portfolio is a priority.
- You want several PowerEdge form factors, from PCIe systems to dense SXM platforms.
- A published, configuration-specific benchmark is useful for initial screening.
- Your operations team already standardizes on the PowerEdge management stack.
HPE ProLiant Compute options
ProLiant Compute DL380a Gen12
The DL380a Gen12 is a 4U accelerator-optimized server using Intel Xeon 6 processors. It supports up to ten double-wide GPUs through PCIe 5.0. Ten double-wide cards can create a different balance of density, host bandwidth, slot topology and power draw than an eight-GPU SXM system. Confirm which GPU models, card widths, auxiliary power connectors and network adapters are supported together in the specific configuration.
XD685
The XD685 is an eight-way GPU platform for large-scale AI training, tuning and inference. HPE lists air-cooled and direct-liquid-cooled configurations, as well as H200 and AMD Instinct MI300X/MI325X options. The cooling choice is not cosmetic: it affects rack plumbing, facility readiness, service procedures, acoustic conditions and the sustained power envelope. A quote must identify the cooling SKU, GPU model, power-supply set and required facility connections.
Management and security
HPE provides iLO 6, a baseboard management controller and Redfish interfaces for hardware monitoring, inventory, remote control and automation. Redfish compatibility can simplify integration with existing provisioning systems, but validate the fields and actions exposed for GPU telemetry, firmware updates and liquid-cooling monitoring before standardizing an automation workflow.
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- You need an accelerator-focused 4U server or an eight-GPU platform.
- Direct-liquid cooling is an option you may need for sustained high-density deployments.
- Your organization relies on iLO, BMC and Redfish operations.
- You want to evaluate NVIDIA and AMD accelerator choices within one platform family.
Lenovo ThinkSystem options
ThinkSystem SR675 V3
The SR675 V3 is a 3U AMD EPYC platform. Lenovo describes base or dense modules supporting up to four or eight double-wide PCIe GPUs rated up to 600 W, plus an SXM5 model with a four-GPU HGX H100 or H200 complex. It supports up to 6 TB of DDR5 memory and redundant power supplies up to 2,600 W. Those figures make host-memory capacity and power planning explicit design variables for data-heavy training and inference.
ThinkSystem SR680a V3
The SR680a V3 is an 8U platform with two fifth-generation Intel Xeon Scalable processors. Lenovo lists eight H100 700 W GPUs with 80 GB HBM3 or eight H200 700 W GPUs with 141 GB HBM3. The larger chassis and 700 W accelerator rating should be mapped to rack power, cooling and service clearances before ordering.
Management and platform flexibility
Lenovo uses XClarity Controller 2 and related XClarity software for remote management and fleet operations. The SR675 V3’s EPYC base and the SR680a V3’s Xeon design give buyers a CPU-platform choice, but CPU flexibility does not remove the need to match memory channels, PCIe topology and network bandwidth to the workload.
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Where Lenovo fits best
- You want EPYC or Xeon choices across the evaluated systems.
- A 3U four- or eight-GPU PCIe design fits your rack plan.
- You need an HGX H100/H200 option or an 8U eight-GPU SXM5 system.
- Your operations team uses XClarity Controller 2 and XClarity software.
Air cooling or direct-liquid cooling?
Choose air cooling when
- Your rack and room can continuously remove the planned heat load.
- Facility teams prefer conventional service procedures and no liquid distribution infrastructure.
- The selected GPU count and power limit remain within the server’s validated air-cooled configuration.
Choose direct-liquid cooling when
- Sustained accelerator power makes air temperature, fan speed or rack density difficult to control.
- The datacenter already has supply, return, leak detection and maintenance procedures for liquid loops.
- You are willing to validate facility-side heat exchangers, hose routing and service clearances.
HPE explicitly lists both air and direct-liquid options for the XD685. Other vendors and models may have different validated choices. Never assume that a liquid-cooled GPU SKU can be transplanted into an air-cooled chassis, or that a server’s maximum GPU count is available at every power and cooling setting.
Remote management, security and fleet operations
| Vendor | Management interfaces identified for these systems | Questions to ask during evaluation |
|---|---|---|
| Dell | PowerEdge management stack | Which console, Redfish endpoints, GPU telemetry and firmware orchestration features are included in the quoted system? |
| HPE | iLO 6, BMC and Redfish | Can your existing automation inventory GPUs, update firmware and monitor cooling alarms through supported interfaces? |
| Lenovo | XClarity Controller 2 and related XClarity software | How will XClarity integrate with your provisioning, alerting, access-control and compliance systems? |
Management quality is operational, not cosmetic. A team running dozens of GPU nodes needs consistent inventory, event logs, remote console access, firmware control, role-based access and an auditable replacement process. Demonstrate those workflows on the exact hardware rather than accepting a generic platform presentation.
What the published benchmark does—and does not—prove
A Dell Technologies and Principled Technologies comparison from 2023 reported up to 4.25 times higher MLPerf v3.1 performance for an XE9680 with H100 SXM5 GPUs than an HPE XL675d Gen10 Plus with A100 SXM4 GPUs in an eight-GPU comparison. The result is useful historical evidence because it names the systems, accelerator generations and test suite.
It is not a universal vendor ranking. The Dell system used newer H100 SXM5 accelerators, while the HPE comparison used A100 SXM4. The result also depends on model, precision, batch size, software versions, drivers, interconnect and benchmark harness. Use it to justify a matched proof-of-concept, not to predict your production throughput.
How to run a fair proof-of-concept
- Use the same model checkpoint, tokenizer, precision and sequence length.
- Keep batch size, accumulation steps and concurrency constant, or document why they differ.
- Record GPU memory use, host-memory use, GPU utilization, interconnect traffic and power.
- Test both single-node scaling and the multi-node network path if training spans servers.
- Repeat enough runs to separate warm-up effects from steady-state throughput.
- Report time to train, tokens per second, latency at the target concurrency and failure or throttling events.
A workload-first buying process
1. Classify the workload
Separate pretraining, fine-tuning, retrieval-augmented inference, batch inference and interactive serving. Training and fine-tuning often emphasize GPU-to-GPU communication and memory capacity. Inference may prioritize memory per GPU, latency, concurrency, power efficiency or the number of independent model replicas.
2. Set the memory requirement
Calculate model weights, optimizer states, activations, KV cache and framework overhead. If the model cannot fit in one GPU, define the required tensor, pipeline or data parallelism before selecting a chassis. H200 and H100 configurations should be compared using the memory capacity actually available in the quoted SKU, not the family name.
3. Select PCIe or SXM
Use PCIe when standard expansion, modularity or a four- to eight-card layout fits the application. Use an SXM or HGX design when tightly coupled multi-GPU operation is central and the organization can support the associated chassis, firmware and cooling requirements.
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4. Verify host resources
Check CPU socket count, core allocation, DDR5 capacity, PCIe lanes, local storage and network-adapter placement. Lenovo’s SR675 V3, for example, is listed with up to 6 TB of DDR5; a similar host-memory requirement should be written into the specification rather than inferred from GPU capacity.
5. Size power and cooling
Use the exact GPU wattage, CPU configuration, memory population, drives, network cards and redundancy mode. Lenovo lists 700 W GPUs in the SR680a V3 and up to 2,600 W redundant power supplies in the SR675 V3; those numbers illustrate why a rack-level electrical study is mandatory. Include startup behavior, sustained load, circuit limits and neighboring equipment in the calculation.
6. Choose the network fabric and storage path
Specify the adapter count, link speed, topology, storage throughput and checkpoint location needed by the application. The cited product information does not establish one vendor as the network or storage leader, so require each bidder to document the exact adapters, slot locations and supported topology.
7. Validate operations
Test remote console access, inventory, firmware updates, GPU telemetry, alert routing, user roles and recovery after a failed node. Compare Dell’s PowerEdge stack, HPE’s iLO 6/BMC/Redfish interfaces and Lenovo’s XClarity tools against the systems your administrators already operate.
8. Demand matched performance data
Ask for results on your model and target precision. Include the software versions, drivers, libraries, batch, concurrency and network configuration in the acceptance criteria.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Total cost and datacenter requirements
Public vendor pages do not provide comparable transaction pricing for these AI systems. Hardware cost is only one part of total cost. Build a complete bill of materials that includes GPUs, CPUs, memory, local storage, network adapters, power supplies, rails, cooling equipment, rack units, facility work, support coverage and software subscriptions or enablement.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsDensity can reduce rack count while increasing per-rack power and cooling complexity. An 8U eight-GPU system may have a different facility cost from a 3U or 4U PCIe server even when both meet the same accelerator count. Direct-liquid cooling can improve thermal headroom but introduces plumbing, monitoring and maintenance obligations. Price those obligations instead of treating cooling as a free option.
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Common mistakes and how to avoid them
Comparing GPU counts without generations
Eight H100 SXM5 GPUs and eight A100 SXM4 GPUs are not equivalent. Record accelerator generation, memory, form factor and interconnect in every comparison sheet.
Ignoring the cooling SKU
“Eight GPUs supported” does not prove that eight high-wattage GPUs run in the selected air-cooled configuration. Require a validated thermal and power bill of materials.
Under-sizing host memory
Data loading, preprocessing, caching and checkpointing can exhaust system RAM before GPU memory is full. Size host memory and storage from the pipeline, not only from model parameters.
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Assuming management tools are interchangeable
Redfish is a standard interface, but implementation details, telemetry and update workflows differ. Test the exact endpoints and permissions your automation needs.
Generalizing one benchmark
The 4.25-times MLPerf result and the 31,000-tokens-per-second XE7745 result are configuration-specific. They cannot predict every model, precision or deployment pattern.
Leaving expansion headroom undefined
Document whether future growth means more GPUs, faster network adapters, additional memory, liquid cooling or a second server. A chassis that is dense today may leave fewer upgrade paths tomorrow.
Final decision framework
- Choose Dell PowerEdge XE platforms when a documented H100/H200 lineup, several PCIe and SXM choices, and published benchmark evidence are primary requirements.
- Choose HPE ProLiant Compute when the DL380a Gen12 or XD685 topology fits, iLO-based operations matter, and air or direct-liquid cooling flexibility is important.
- Choose Lenovo ThinkSystem when EPYC or Xeon flexibility, dense four- or eight-GPU layouts, HGX H100/H200 options or XClarity management align with your standards.
- Choose none by brand alone until a proof-of-concept confirms GPU memory, interconnect scaling, host resources, network behavior, cooling and operational workflows for the actual workload.
Bottom line
Dell, HPE and Lenovo all offer credible AI server paths, but they optimize different combinations of accelerator topology, density, cooling and management. Start with the model and service-level target, select the GPU architecture and interconnect, then choose the chassis that can sustain the required power and operating procedures. A matched benchmark and a complete facility-ready bill of materials are more reliable decision tools than a generic vendor ranking.
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