Model	Form Factor	CPU Options	Memory Capacity	Storage Bays	GPU Slots
NF5466M6	4U	2× Intel Xeon Gold (up to 28 cores ea.)	Up to 2 TB DDR4	24×3.5″ hot-swap	4× FH PCIe
NF5466M5	4U	2× Intel Xeon Silver/Gold	Up to 1 TB DDR4	24×3.5″ hot-swap	2× FH PCIe
NF5468M6	4U	2× Intel Xeon Silver/Gold	Up to 1 TB DDR4	16×3.5″ + GPU trays	8× FH PCIe
NF8480M6	2U	1× Intel Xeon Gold 5315Y/6330A	Up to 512 GB DDR4	8×2.5″ NVMe	2× FH PCIe
NF8260M5	2U	2× Intel Xeon Gold	Up to 512 GB DDR4	8×2.5″ SAS/SATA	8× FH PCIe
NF5280M6	2U	1× Intel Xeon Silver/Gold	Up to 256 GB DDR4	8×2.5″ SAS/SATA	4× LP PCIe
NF5270M6	2U	2× Intel Xeon Silver	Up to 256 GB DDR4	8×2.5″ or 4×3.5″	–

Category	Model Series	Form Factor	CPU Family	Max GPUs	Drive Bays
X14 Servers	SYS-112C-TN, SYS-112H-TN, SYS-122H-TN, SYS-112B-WR	1U	Intel Xeon Scalable Gen4	–	Up to 4× U.3 or 8×2.5″
	SYS-212H-TN, SYS-222H-TN, SYS-522B-WR	2U	Intel Xeon Scalable Gen4	–	Up to 12× U.3 or 24×2.5″
GPU Servers	AS-4124GO-NART+	4U	Intel Xeon Scalable	4–8	12× U.3 + GPU trays
	AS-4125GS-TNRT2, AS-5126GS-TNRT, AS-5126GS-TNRT2	4U/5U	Intel Xeon Scalable H13/H14	8	16× U.3 + GPU trays
AMD Servers	AS-1115CS-TNR-G1, AS-1115HS-TNR-G1, AS-1125HS-TNR-G1	1U	AMD EPYC™ 7003/7004 Series	–	Up to 8×2.5″
	AS-2015CS-TNR-G1, AS-2015HS-TNR	2U	AMD EPYC™ 7003/7004 Series	–	Up to 12×2.5″

Component	Specification
Model	#BPL-305 Balance 305
Part Number (PN)	BPL-305
WAN Ports	3× 10/100/1000 Mbps GE SFP ports
LAN Ports	3× 10/100/1000 Mbps GE SFP ports
Bypass Ports	2× LAN bypass ports
Throughput	1 Gbps aggregate forwarding
SpeedFusion Peers	2 peers (expandable to 30 with BPL-305-SPF license)
Management	LCD panel, web interface, InControl 2 cloud management
Form Factor	1U rackmount (19″), USB console, optional rack ears
Power	100–240 VAC input, redundant support

Series	Form Factor	Uplinks	Stack/Chassis	Throughput
Catalyst 9300	1U fixed	4× 10G/25G	StackWise-480 (8 members)	480 Gbps
Catalyst 9400	Chassis (5-slot)	10G/25G modules	5-slot, field-replaceable	9 Tbps
Catalyst 9500	1U fixed	4× 40G/100G	N/A	25.6 Tbps
Catalyst 9600	Chassis (3-slot)	40G/100G modules	3-slot, redundant SUPs	25.6 Tbps
Nexus 3000	1U fixed	10/25/40/100G	N/A	2.56 Tbps
Nexus 3550	1U fixed	100G QSFP28	N/A	3.2 Tbps
Nexus 7000	Chassis (8-slot)	10/40G modules	8-slot with redundant SUP	28.8 Tbps
Nexus 9000	1U/2U fixed	25/50/100G	N/A	25.6 Tbps
Business 110	Desktop/1U	Unmanaged 1G	N/A	Up to 128 Gbps
Business 250	1U fixed	10G uplinks	N/A	Up to 176 Gbps
Business 350	1U fixed	10G uplinks	Layer 3 Lite	Up to 280 Gbps

Component	Specification
Model	SA5456M5
CPUs	2× Intel® Xeon® Gold 6152 (22 cores/44 threads, 2.10 GHz/3.70 GHz)
Memory	12×64 GB DDR4 RDIMM @ 2666 MT/s (768 GB total)
Storage	60×16 TB Seagate® Exos® X16 HDDs (960 TB raw)
Networking	1× Intel® XL710-QDA1 40 GbE QSFP+ PCIe 3.0 x8
Chassis	4U rack-mountable; hot-swap 3.5″ drive bays; redundant PSUs and fans
Expansion Slots	8× PCIe 3.0 slots for OCP/GPU/NVMe modules
Management	IPMI with web GUI and Redfish API support

Component	Description	Part Number (PN)
Chassis	ATCA 16-slot chassis with shelf managers and fan modules	C112648
CPU Blade	Dual-socket central processing unit blade	C112772
Storage Module	Storage blade with SAS HDDs	C112773
Power Module	Shelf power module	C112695
Hub Blade	Connectivity blade for ATCA system	C112671
Rear Transition Module	RTM for hub blade	C112670
Carrier Blade	AMC carrier blade	C111975
DSP Blade	Digital signal processor blade	C112635

Lenovo WA5480G3 8-GPU AI/Compute Server Configuration...

Derecelendirme: 4

Lenovo WA5480G3 8-GPU AI/Compute Server Configuration

Ürün Kategorileri:Sunucular
Parça numarası:Lenovo WA5480G3
Mevcudiyet:In Stock
Durum:Yeni
Ürün özellikleri:Gönderilmeye Hazır
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Lenovo WA5480G3 8-GPU AI/Compute Server Configuration

Keywords

Lenovo WA5480G3, 8 GPU server, Xeon Gold 6530, 768 GB DDR5 ECC RAM, NVMe storage array, PERC H755 RAID, Nvidia H200 NVL, ConnectX-7 400G, AI training server

Description

The Lenovo WA5480G3 is a high-density 4U AI / compute server engineered for demanding workloads like model training, deep learning inference, and large data analytics. This variant supports **8 × Nvidia H200 NVL GPUs**, coupled with a balanced CPU and storage architecture to avoid bottlenecks and maintain maximum throughput.

At its core, it houses dual **Intel® Xeon® Gold 6530** processors, offering a high core count and frequency to feed GPU workloads. With **32 modules of 64 GB DDR5 ECC memory** (totaling 2,048 GB), the system ensures abundant capacity for GPU-resident datasets, caching, and model parameters.

Storage is robust: **24 × 7.68 TB NVMe SSDs** for ultra-fast direct I/O, plus a **2 × 480 GB M.2 SSD** array in RAID mode for OS, boot, and metadata. Networking is handled by **Nvidia ConnectX-7 400 Gbps** for high-speed GPU interconnect or data egress, and additional 4 × 1 GbE ports. Redundant **4 × 2700 W power supplies** guarantee continuous operations even under heavy GPU loads.

This configuration is ideal for training large neural networks, handling massive batch inferencing, or running hybrid workloads where both CPU and GPU cycles are critical. The system is designed for maximum GPU density while preserving I/O, memory, and power balance across the platform.

Key Features

Dual Intel Xeon Gold 6530 processors for compute and coordination
2,048 GB (32 × 64 GB) DDR5 ECC memory, enabling large dataset caching
24 × 7.68 TB NVMe drives for high throughput and low latency storage
2 × 480 GB M.2 SSDs in RAID for OS / boot / metadata performance
8 × Nvidia H200 NVL GPUs (Part Number 900-21010-0040-000), optimized for AI training and inference
4 × Nvidia 2-Way NVLink bridges (900-53651-0000-000) to connect GPUs with high bandwidth
Single-port 400 Gbps ConnectX-7 NIC (MCX75510AAS-NEAT) for GPU / data path connectivity
4 × 1 GbE PCIe network ports for management and control traffic
Redundant 4 × 2700 W power supplies for failover and sustained power delivery
Rail kit support for 4U rack mounting and serviceability

Configuration

Component	Specification / Part Number
Model / Chassis	Lenovo WA5480G3 (4U, GPU AI platform)
Processor	2 × Intel Xeon Gold 6530
Memory	32 × 64 GB DDR5 ECC (2,048 GB total)
NVMe Storage	24 × 7.68 TB NVMe SSD
M.2 SSD (RAID)	2 × 480 GB M.2 SSD in RAID
GPU Accelerators	8 × Nvidia H200 NVL (PN 900-21010-0040-000)
NVLink Bridges	4 × Nvidia 2-Way NVLink (PN 900-53651-0000-000)
High-Speed NIC	1 × Nvidia ConnectX-7 400 Gbps (MCX75510AAS-NEAT)
Ethernet Ports	4 × 1 GbE via PCIe
Power Supplies	4 × 2700 W redundant PSU
Accessories	Rail Kit, cabling, GPU risers, cooling system

Compatibility

The Lenovo WA5480G3 is marketed in Lenovo’s “WenTian / Qtian” AI server line, built to support 4th Gen or 5th Gen Intel Xeon Scalable platforms.

The system supports PCIe Gen5 expansion suitable for modern GPUs and accelerators.

Lenovo has certified this platform for multiple operating systems and AI stacks.

Common supported OS include Red Hat Enterprise Linux, SUSE Linux Enterprise, Ubuntu, and virtualization/hypervisor systems.

The NVMe storage subsystem and GPU interconnects are compatible with VMD, PCIe switch architectures, and GPU direct access paths. The ConnectX-7 400G NIC supports modern RDMA and high throughput networking needs.

Software frameworks including CUDA, ROCm (if supported), TensorFlow, PyTorch, and distributed training frameworks (e.g. NVIDIA NCCL, Horovod) are expected to interoperate on this architecture with proper driver support.

Usage Scenarios

This 8-GPU configuration is ideal for **AI / deep learning model training** tasks, especially those requiring large memory and high GPU throughput concurrently. The dual CPU setup helps coordinate GPU workloads while feeding data pipelines efficiently.

It’s also suited for **hybrid inference + training clusters**, where portions of the workload run on CPU (pre-processing, orchestration) and the heavy lifting is done by GPUs. The 400 Gbps NIC enables high bandwidth data distribution across nodes or storage systems.

Use cases such as **large language model fine-tuning**, **computer vision training with high dimensionality**, **reinforcement learning workloads**, and **multi-tenant AI services** will benefit from this design.

The system is also applicable in **HPC / simulation workloads** that can leverage GPU acceleration, or **data analytics pipelines** that combine CPU and GPU compute stages in a single server node.

Frequently Asked Questions (FAQs)

1. Can this Lenovo WA5480G3 configuration support PCIe bifurcation or switch topologies for the 8 GPUs?

Yes. The WA5480G3 is built to support multiple expansion topologies including PCIe switch or direct CPU lanes, enabling flexible GPU interconnect strategies with NVLink or PCIe bridges.

2. How do I optimize storage performance when using 24 NVMe drives?

You can configure NVMe arrays as JBOD, RAID via software (e.g. RAID5/6, erasure coding), or employ caching layers. The SSDs or M.2 RAID system help host boot volumes or metadata, leaving NVMe drives dedicated to data I/O.

3. What cooling and power considerations are necessary with 8 GPUs and 4 × 2700 W supplies?

This server will require robust airflow and thermal design. The redundant 2700 W supplies ensure headroom under maximum GPU load. Ensure rack airflow, hot-aisle/cold-aisle alignment, and power distribution are sized for >10 kW per node.

4. Which frameworks and libraries are supported on this hardware?

The system supports CUDA, cuDNN, NCCL for GPU computing, and distributed AI frameworks such as TensorFlow, PyTorch, Horovod, and MXNet. The ConnectX-7 NIC supports RDMA for efficient inter-node communication. Ensure you use up-to-date drivers and libraries matching kernel and firmware versions.