Best GPU Servers for Machine Learning

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Jul 17
3 min read

Updated: Jul 25

Training machine learning models means processing large datasets, tuning billions of parameters, and managing growing memory and bandwidth requirements. Whether you’re working on classical ML, deep neural nets, or generative models, performance starts with the right server.

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This article walks you through the best GPU servers for machine learning, including the latest GPUs like the NVIDIA B200 and H200, AMD’s MI350 Series, and AI-optimized platforms from Dell, HPE, Lenovo, and Supermicro.

Best GPU servers for machine learning and AI training in 2025 featuring NVIDIA H200, B200, GB200 Grace Blackwell, and AMD MI350 accelerators, ideal for deep learning, LLMs, generative AI, and high-performance computing workloads with support for NVLink, PCIe Gen5, and liquid cooling. server-parts.eu refurbished

Best GPU Servers for Machine Learning: Machine Learning Server Requirements

Here are the core components of an effective machine learning training server:

GPUs (Machine Learning Accelerators)

NVIDIA H200 & B200: The H200 delivers up to 4.8 TB/s memory bandwidth, significantly outperforming the H100. The B200, built on the Blackwell architecture, pushes ML training further with FP4/FP6 optimization.
NVIDIA GB200 Grace Blackwell: Combines next-gen GPUs with Arm CPUs and shared memory pools. Ideal for large, parallel training tasks.
AMD MI350 Series: Offers up to 288 GB of HBM3E memory and 8 TB/s of bandwidth. Great for multi-GPU ML workloads.

CPU & Memory

Dual-socket AMD EPYC Genoa/Bergamo or Intel Xeon Emerald Rapids
32–192 cores and up to 4 TB DDR5 RAM
Fast PCIe Gen5 lanes are essential for low-latency GPU access.

Interconnects

NVIDIA NVLink 4.0 / NVSwitch for intra-server GPU bandwidth
InfiniBand NDR (400 Gb/s) or HDR (200 Gb/s) for distributed machine learning clusters

Storage & I/O

PCIe Gen4/Gen5 NVMe SSDs, 8 to 24 bays typical
Storage throughput is key for loading large datasets during training

Power & Cooling

GPUs like H200 and MI350 draw 400–700 W each
Enterprise setups use dual 3 kW PSUs and liquid/advanced air cooling

Best GPU Servers for Machine Learning: Server Recommendations

Entry Tier (1–2 GPUs)

Use Case: Prototyping, academic research, fine-tuning smaller models

Cost: $6,000–$22,000

Brand	Model	Notes
Dell	PowerEdge R660 / R760	Supports 1–2x H200/B200/MI350, good for ML labs and entry setups
HPE	ProLiant DL385 Gen12	Dual EPYC CPUs, up to 2 GPUs, solid RAM options
Lenovo	ThinkSystem ST250 V3	Tower format, quiet operation, ML-ready with 2x GPU slots

Standard Tier (3–6 GPUs)

Use Case: Multi-model training, enterprise PoCs

Cost: $25,000–$60,000

Brand	Model	Notes
Dell	PowerEdge R760xa	4x GPUs (H200 or B200), PCIe Gen5, 2 TB DDR5
HPE	Apollo 6500 Gen12	Supports 4–6x GPUs, InfiniBand-ready
Lenovo	ThinkSystem SR675 V3	AMD EPYC 9004, up to 6 GPUs, excellent airflow and NVMe support

High-End Tier (8+ GPUs or GPU Clusters)

Use Case: LLMs, multimodal ML systems, foundation model training

Cost: $60,000–$250,000+

Brand	Model	Notes
Dell	PowerEdge XE9680	8x NVIDIA H200 or B200 SXM, dual EPYC CPUs, NVLink
HPE	Apollo 6500 Gen12	Dense cluster with liquid cooling and 8x GPUs
Lenovo	ThinkSystem SR670 V2	High-memory system with up to 4 TB DDR5, ideal for training LLMs
Supermicro	SYS-522GA-NRT	8x H200 NVL, advanced interconnects, used for FP4 compute clusters
Supermicro	H14 Gen (AMD MI350)	Supports large MI350-based racks, scalable to 64 GPUs per pod

Best GPU Servers for Machine Learning: Summary

Machine learning demands powerful infrastructure:

Top GPUs like NVIDIA B200, H200, GB200 and AMD MI350 Series
AI-optimized servers from Dell, HPE, Lenovo, and Supermicro
High-speed PCIe Gen5 NVMe, InfiniBand NDR, and liquid cooling

Don’t overlook cloud-based alternatives like AWS, Lambda Labs, or Google Cloud—especially useful for short-term scaling or experimental workloads.

Choose based on your real-world training needs. Whether you’re testing models in a lab or training foundation models, the right GPU server saves time, energy, and budget.