Performance and Efficiency at Every Scale

Building on Intel's extensive experience in accelerator design and deep expertise in microarchitecture and software, Intel extends its AI acceleration capabilities across a broader range of applications with two flexible deployment options based on form factors: an 8-GPU UBB or a traditional dual-slot PCIe card. Designed for scalability and versatility, the Intel Gaudi 3 platform uses an open software ecosystem, optimized to meet diverse performance, power, and budget requirements. from large-scale AI factories to highly regulated industries.

Scaling AI with the Intel Gaudi 3 AI Accelerator in GIGABYTE Servers

Engineered for large-scale AI models across hyperscalers, clusters, and enterprises, the combination of Intel Gaudi 3 AI accelerator OAM card (HL-325L) and universal baseboard (HLB-325) delivers exceptional performance for large-scale AI training and inference. With advanced compute technologies and networking, it meets the needs of AI research, hyperscale computing, and cloud environments. GIGABYTE has custom built an 8U air-cooled server, G893-SG1-AAX1, featuring an optimized thermal design, providing the robust infrastructure required for high-performance AI acceleration within a standard air-cooled server.

Designed for enterprise AI applications, the Gaudi 3 PCIe card (HL-338) is ideal for AI inference, secure on-prem workloads, and cost-efficient AI deployments. With improved power efficiency and a compact PCIe form factor, it enables organizations to scale AI efficiently. GIGABYTE provides compatible 2U and 4U servers that integrate seamlessly into existing infrastructures, optimizing rack space and reducing operational overhead.

GIGABYTE is ready to deliver end-to-end solutions for the Intel Gaudi 3 AI Accelerator. From design to deployment, our optimized server platforms ensure seamless scalability and rapid time-to-value, so you can deploy AI infrastructure whenever your business is ready to move forward.

Intel Gaudi 3 UBB (8 x OAM cards)

G893-SG1-AAX1

Intel Gaudi 3 PCIe

G494-SB0-AAP2

Designed for the Real-World Demands of AI

Scalable Performance for Every AI Need

Delivers powerful compute performance and memory bandwidth for workloads from enterprise inference to massive LLMs, and adaptable across scales and industries.

Flexible Deployment for Any Infrastructure

A unified architecture with OAM UBB and PCIe options that integrate seamlessly into diverse data centers, optimizing for space, power, and performance.

Optimized Power and Thermal Efficiency

Engineered to balance high throughput and low energy use, enabling reliable, high-performance AI acceleration in standard air-cooled systems.

Open and Ready Software Ecosystem

Supports leading AI frameworks and precision formats including FP8 and BF16 with day-zero access to top LLMs, accelerating deployment and developer productivity.

Intel Gaudi 3 AI Accelerator Specifications

HL-325L OAM Mezzanine Card

HL-338 PCIe Add-In Card

Product	HL-325L OAM Mezzanine Card	HL-338 PCIe Add-In Card
Architecture	5th Generation Tensor Processor Core
Supported Datatypes	FP32, BF16, FP16 & FP8 (E4M3 / E5M2)
MME Units	8
TPC Units	64
HBM Capacity	128 GB
HBM Bandwidth	3.7 TB/s
On-die SRAM Capacity	96 MB
On-die SRAM Bandwidth (read/write)	12.8 / 6.4 TB/s
Networking (bidirectional)	1200 GB/s
Host Interface	PCIe Gen5 x16
Host Interface Peak Bandwidth	128 GB/s (64 GB/s per direction)
Media Decoders	14
TDP	900W	600W
Form Factor	OCP OAM 2.0 Mezzanine card	FHFL Dual-slot PCIe Gen5 x16 card
System Configuration	8-card connected with HLB-325 universal baseboard	1 group of 4-card via top board (HLTB-304) or 2 groups of 4-card via top boards (2 × HLTB-304)
Networking (Scale-up/Card-to-Card)	21 × 200GbE through HLB-325 universal baseboard	18 × 200GbE through HLTB-304 Top Board
Networking (Scale-out)	3 × 200GbE through HLB-325 universal baseboard	Through Host-NIC

*The HLTB-304 board allows connectivity of 4 × HL-338 cards through 6 × 200GbE links from each card to the other cards, 18 links of 200GbE total per card.

 

Applications for AMD EPYC™ 8004 Series Processors

HPC

Complex problem-solving in HPC applications use numerical methods, simulations, and computations to achieve significant insights. While traditionally less dependent on GPUs, the overwhelming parallel computing power of GPGPUs has greatly accelerated the development of HPC in recent years, making hybrid configurations a growing trend in modern supercomputers.

AI

With the rapid adoption of AI, from general applications to the fast-evolving deep learning, GPGPUs have become a game changer for the industry. The parallel processing capabilities of GPGPUs allow for the handling of massive datasets and complex algorithms, which are essential for training and deploying AI models. As a result, AI has become the key to making modern systems faster and “smarter” in the most efficient way.

Data Analytics

In data-intensive applications such as big data and computational simulations, systems rely heaving on GPGPUs for high parallel processing, low latency, and high bandwidth to facilitate data mining and large-scale data processing. The ability of GPGPUs to handle vast amounts of data simultaneously not only accelerates the processing of massive datasets but also enables more accurate and timely insights, driving informed decision-making in fields like finance, healthcare, and scientific research.

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