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

NVIDIA H200 NVL 141GB PCIe GPU Accelerator (Part Number 900-21010-0040-000) for Generative AI & HPC...

Ocena: 4

NVIDIA H200 NVL 141GB PCIe GPU Accelerator (Part Number 900-21010-0040-000) for Generative AI & HPC

Kategorie produktów:Komponenty
Numer katalogowy:NVIDIA H200 NVL 141GB
Dostępność:In Stock
Stan:Nowy
Cechy produktu:Gotowy do wysyłki
Minimalne zamówienie:1 szt.
Cena katalogowa wynosiła:$39,999.00
Twoja cena: $30,715.00 Oszczędzasz $9,284.00
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Title

NVIDIA H200 NVL 141GB PCIe GPU Accelerator (Part Number 900-21010-0040-000) for Generative AI & HPC

Keywords

NVIDIA H200 NVL, 141GB HBM3e memory, PCIe Gen5 x16 accelerator, AI inference GPU, Hopper architecture GPU, buy H200 NVL, enterprise GPU accelerator, 900-21010-0040-000

Description

The NVIDIA H200 NVL (PN 900-21010-0040-000) is a premium GPU accelerator card built on NVIDIA’s Hopper architecture. It features **141 GB of HBM3e** ultra-fast memory with **4.8 TB/s memory bandwidth**, delivering exceptional performance for generative AI inference, large language models (LLMs), and high-performance computing (HPC) workloads.

The PCIe Gen5 ×16 interface allows this accelerator to be deployed in a wide range of servers that support modern PCIe slots, providing high throughput and compatibility. With part number 900-21010-0040-000, this unit is ideal for OEM integrators or existing GPU server upgrades.

Its design supports multiple instances per GPU via NVIDIA’s MIG-style partitioning, enabling flexible usage for multi-tenant inference, virtualized deployment, or fine-tuning smaller models. The higher memory and bandwidth make it well-suited to handle larger model weights and data without frequent data swaps.

Power requirements are significant: TDP can go up to ~600–700W depending on configuration and cooling. Ensure that server power delivery, cooling, and form factor can support this module.

For enterprises planning to buy H200 NVL, this card represents a major leap forward over previous-generation GPUs in memory capacity, inference throughput, and overall efficiency for AI and HPC tasks. It’s especially valuable for inference farms, model serving, large-scale data science, or multi-GPU cluster use.

Key Features

141 GB of HBM3e GPU memory with **4.8 TB/s memory bandwidth** for high throughput. 141GB HBM3e memory
Supports PCIe Gen5 ×16 form factor for broad compatibility. PCIe Gen5 x16 accelerator
High precision and mixed precision support: FP64, FP32, TF32, BFLOAT16, FP16, INT8, FP8, with Tensor Cores to accelerate deep learning and scientific computing.
Multi-Instance GPU (MIG-style) support for partitioning resources per task or user, enabling flexible usage.
High interconnect options: NVLink or NVLink bridge in multi-GPU setups to allow very high bandwidth between GPUs.
Passive or air-cooled PCIe form (in NVL version) enabling deployment in standard server racks without liquid cooling for certain configurations.

Configuration

Component	Specification / Details
Part Number / Model	NVIDIA H200 NVL – PN 900-21010-0040-000
GPU Memory	141 GB HBM3e
Memory Bandwidth	≈ 4.8 TB/s
Interface	PCI Express Gen5 ×16 (NVL passive PCIe form)
Precision & Compute	FP64, FP32, TF32, BFLOAT16, FP16, INT8, FP8; Tensor Cores included.
Multi-Instance Support	Up to 7 MIGs / instances depending on configuration.
Power Consumption	Up to ~600–700 W depending on workload / cooling.
Cooling & Form Factor	Passive/air-cooled PCIe form (NVL version) or SXM boards in some systems with NVLink.
Interconnect / Bridges	NVLink bridges for multi-GPU, PCIe Gen5 for host I/O.
Supported Software / Stack	NVIDIA AI Enterprise, CUDA, cuDNN, TensorRT, ONNX, etc.

Compatibility

The NVIDIA H200 NVL requires a server with a free PCIe Gen5 x16 slot and sufficient power delivery capacity (600-700W headroom), robust cooling (air flow, fan capacity), and a compatible BIOS / firmware that supports recent NVIDIA drivers.

It is supported in many NVIDIA-Certified systems, including those from Lenovo ThinkSystem, Dell, and other OEMs that list H200 NVL as a certified option. LGX / MGX / HGX NVLink options exist where NVLink bridges are used for multi-GPU setups.

Software compatibility includes Linux distributions like Ubuntu 24.04 LTS, Red Hat Enterprise Linux 9.x, and others; drivers via NVIDIA CUDA / AI Enterprise stack. Verify that your kernel and OS support the Hopper architecture and necessary driver versions.

Usage Scenarios

1) Large Language Model (LLM) Inference & Serving: With 141 GB memory and high bandwidth, the H200 NVL can serve large-parameter models with fewer memory bottlenecks, enabling faster inference and reduced context-switch overhead. Ideal for AI-as-a-service or APIs.

2) High-Performance Computing (HPC) and Scientific Simulations: Workloads involving FP64, large matrix operations, computational fluid dynamics, and climate modeling will benefit from the Hopper architecture and HBM3e bandwidth.

3) AI Training (Fine-Tuning) & Mixed Precision Workloads: While full training of massive models might use SXM versions, the NVL form is excellent for fine-tuning or mixed precision training, and offers good memory bandwidth and size.

4) Data Center GPU Cloud Infrastructure: In multi-tenant GPU clouds, the ability to partition the H200 into multiple instances (MIG/other partitioning) helps allocate GPU resources efficiently. Also useful for GPU aggregation nodes.

5) Graphics / Rendering / Visualization: For high resolution rendering, 3D simulation, VR/AR content, scientific visualization, the large memory buffer and high bandwidth can hold large datasets and texture assets.

Frequently Asked Questions

Q: What is the difference between H200 NVL and H200 SXM / HGX versions?
A: The NVL version is a PCIe form-factor variant; it tends to have passive or air cooling and is designed for broader server compatibility via PCIe Gen5. SXM/HGX versions offer interconnects like NVLink/HGX boards, higher power envelope and often better cooling and multi-GPU scaling.
Q: How many GPU partitions (MIG instances) can I run on the H200 NVL?
A: Up to **7 MIG-style instances** are supported depending on configuration; each instance gets ~16.5 GB memory in some partitioning modes. This enables multi-tenant or multi-workload deployment.
Q: What server power supply & cooling should I plan for when installing this GPU?
A: Plan for at least **600-700W** headroom for this GPU under load, plus sufficient cooling in the chassis. Ensure PCIe slot has proper support, airflow is unobstructed, and power connectors meet NVIDIA specifications.
Q: Will the GPU memory be fully usable for large model inference without offloading?
A: Yes—the 141 GB HBM3e provides a large contiguous memory pool, reducing need for offloading or frequent data movement. But actual usable capacity depends on model size, precision, batch size, and any memory reserved by the system or driver overhead. Mixed precision (FP16/FP8) can also help fit larger models.