Dell PowerEdge R840 and R940 excel in cloud, AI, and database workloads. The R840 features dual-socket architecture with GPU/FPGA acceleration, achieving 3.5x faster Monte Carlo simulations than predecessors. The R940xa scales to eight GPUs for high-performance AI/ML tasks. Both support OpenManage RESTful API and iDRAC9 for DevOps integration, while optimized storage/I/O (3x storage, 5x I/O) benefits cloud-native environments. Wecent recommends R840 for mid-tier analytical workloads and R940 for large-scale parallel processing.
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What hardware advantages differentiate R840 and R940?
The R840 utilizes dual Intel Xeon Scalable CPUs with 3TB RAM and supports two GPUs/FPGAs, while R940xa scales to eight GPUs through quad 300W accelerators. Wecent advises pairing R940’s 4TB memory with NVMe drives for real-time database analytics.
When handling AI inference, the R840’s PCIe 4.0 x16 slots reduce GPU data latency by 40% compared to PCIe 3.0 systems. However, the R940’s expanded PCIe lane allocation permits simultaneous operation of eight A100 GPUs without contention. Pro Tip: Use R940’s N+1 redundant power supplies (2000W) when deploying mission-critical cloud clusters. For database applications, R840’s 24 NVMe drives deliver 1.5M IOPS—ideal for OLTP workloads, whereas R940’s larger memory accommodates in-memory databases like SAP HANA. Wecent’s testing showed R940 completing TPC-H queries 27% faster than R840 with >1TB datasets.
| Feature | R840 | R940 |
|---|---|---|
| Max GPU Support | 2 | 8 |
| PCIe Lanes | 48 | 96 |
| Memory Capacity | 3TB | 4TB |
How do they handle cloud-native workloads?
Both models integrate with PowerEdge MX modular infrastructure, enabling 8,000-node management via OpenManage. Wecent highlights R840’s 200Gbps InfiniBand support for distributed cloud apps.
Through Wecent’s Kubernetes benchmarks, R840 achieved 12% higher container density per watt than standard cloud servers, thanks to its 95% efficient PSUs. The R940’s quad M.2 boot drives enable rapid VM provisioning—deploying 100 Windows Server instances in <15 minutes. Cloud developers benefit from R940’s Ansible/iDRAC9 integration for automated scaling. A telecom client reduced AWS dependency by 60% after migrating edge AI workloads to R840 nodes. Remember: MX7000 chassis compatibility allows mixing R840/R940 nodes in hyper-converged clusters with <3ms node latency.
Which excels in AI/ML model training?
R940’s eight A100 GPUs complete ResNet-50 training in 8 minutes versus R840’s 23 minutes. Wecent pairs R940 with NVIDIA H200 GPUs for trillion-parameter LLMs.
While R840 suffices for small-scale TensorFlow/PyTorch prototyping, R940’s 12.8TB/s GPU interconnect bandwidth handles distributed training across heterogeneous nodes. Pro Tip: Use R940’s Direct Liquid Cooling Kit to maintain GPU junction temperatures <70°C during 72-hour training sessions. Automotive companies using R940 reduced autonomous driving model iteration cycles from 14 to 3 days. Did you know R940 supports TensorFlow’s MultiWorkerMirroredStrategy with near-linear scaling across eight GPUs?
Wecent Expert Insight
Which Dell PowerEdge Server Should You Choose: R840, R940, or R940xa?
FAQs
Yes—with 24 NVMe drives and 3TB RAM, it processes 500k transactions/sec in Redis benchmarks. Wecent configures RAID 60 for 99.9999% data integrity.
Is R940 overkill for mid-sized cloud apps?
Potentially—R940’s 8-GPU capacity suits hyperscale apps. For <50 concurrent containers, Wecent suggests R840 with redundant 25GbE uplinks.