Lenovo ThinkSystem SR675 V3 with NVIDIA H200 SXM5 141GB GPU

The Lenovo ThinkSystem SR675 V3 (7D9QA002NA) is a 2-socket, 2U rack server engineered specifically for high-density GPU computing, designed to accommodate up to eight NVIDIA H200 SXM5 141GB HBM3e accelerators in a tightly integrated SXM5 baseboard configuration. Built on the AMD EPYC 9004 series (Genoa) processor platform, the SR675 V3 delivers exceptional CPU-to-GPU bandwidth and leverages PCIe 5.0 interconnects alongside NVLink 4.0 between GPUs, enabling the memory bandwidth and interconnect throughput demanded by frontier AI training and large-scale inference workloads.

The NVIDIA H200 SXM5 141GB GPU at the heart of this configuration introduces HBM3e memory, offering approximately 141GB of GPU memory per accelerator with memory bandwidth reaching 4.8 TB/s per GPU. Across a fully populated eight-GPU node, this translates to over 1.1TB of aggregate HBM3e capacity, making the platform uniquely suited for hosting very large language models (LLMs), multimodal foundation models, and memory-intensive scientific simulations that previously required model parallelism across multiple nodes. The H200 Tensor Core architecture retains full compatibility with the Hopper GPU microarchitecture, supporting FP8, FP16, BF16, TF32, and FP64 precision modes with Transformer Engine acceleration.

The SR675 V3 chassis is optimized for datacenter thermal efficiency at high GPU TDP loads, with front-to-rear airflow management and Lenovo Neptune direct water cooling support to address the substantial power envelopes of a fully loaded H200 SXM5 system. Enterprise connectivity options include high-bandwidth networking via OCP 3.0 and PCIe 5.0 expansion slots, supporting NVIDIA ConnectX-7 or BlueField-3 DPU adapters for InfiniBand NDR and Ethernet 400GbE fabric integration. The platform is a validated building block for Lenovo TruScale AI infrastructure and is qualified for deployment in large-scale GPU clusters serving enterprise AI, HPC, and cloud-native workloads across regulated and commercial environments.

Ideal for

• Training and fine-tuning large language models and multimodal foundation models requiring hundreds of gigabytes of aggregate GPU memory per node
• High-throughput generative AI inference serving for enterprise applications where low latency and large KV-cache capacity are critical
• Computational fluid dynamics, molecular dynamics, and weather simulation HPC workloads that benefit from FP64 Tensor Core performance and large HBM capacity
• Enterprise AI research and development environments requiring dense, scalable GPU clusters interconnected via InfiniBand NDR or high-speed Ethernet fabrics
• Data analytics and in-memory GPU-accelerated database workloads processing very large datasets that exceed the capacity of conventional GPU memory configurations
• Sovereign AI and national research computing deployments in UAE, GCC, EMEA, and APAC regions requiring high-performance, datacenter-grade GPU infrastructure

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.