Interconnect: Integrated 100 Gb Ethernet networking
Software Stack: SynapseAI, PyTorch integrations, TensorFlow support
Organizations that typically deploy Gaudi 2 accelerators include:
The accelerator is particularly attractive for buyers interested in open networking architectures and diversified AI hardware stacks.
Gaudi 2 accelerators are available through OEM server vendors and cloud infrastructure platforms that support Intel’s AI accelerator ecosystem.
Availability varies depending on vendor partnerships and system integrators, though adoption has increased as organizations explore alternatives to GPU-based AI infrastructure.
The Intel Gaudi 2 accelerator is a dedicated AI training processor developed by Habana Labs, a subsidiary of Intel. It was designed to compete with high-performance GPUs in deep learning workloads by offering strong compute performance combined with built-in high-speed networking.
Unlike traditional GPUs, Gaudi accelerators integrate multiple 100 Gb Ethernet ports directly on the chip, enabling large distributed training clusters without requiring separate networking hardware. This architecture simplifies cluster design while enabling high-bandwidth communication between accelerators.
The Gaudi 2 architecture also emphasizes efficient scaling for deep learning workloads. It supports large transformer models, distributed training frameworks, and common machine learning libraries through the SynapseAI software stack.
| Specification | Gaudi 2 Accelerator Details |
|---|---|
| Architecture | Habana Gaudi 2 |
| Compute Units | Tensor Processor Cores |
| Memory | 96 GB HBM2e |
| Memory Bandwidth | ~2.45 TB/s |
| Interconnect | Integrated 100 Gb Ethernet |
| Form Factor | OAM |
| Max TDP | ~600 W |
| Precision Support | FP32, BF16, FP16 |
| Typical AI Compute | ~2 PFLOPS (BF16) |
| Process Node | TSMC 7 nm |
| Transistor Count | ~54 billion |
| On-Chip Networking | 24 × 100 GbE ports |
| Multi-Accelerator Scaling | Ethernet fabric |
In many AI training scenarios, Gaudi 2 delivers performance comparable to GPUs such as the A100 while offering alternative deployment architectures.
The accelerator is designed primarily for AI training workloads that benefit from distributed scaling across many nodes.
Comparable Accelerators:
Upgrade Path:
These platforms compete in the market for large-scale AI training infrastructure.
Related Intel Accelerators:
Competing AI Hardware:
The Intel Gaudi 2 accelerator represents Intel’s approach to high-performance AI training hardware. By combining dedicated tensor compute units, high-bandwidth HBM memory, and integrated Ethernet networking, the architecture enables scalable deep learning clusters without relying on traditional GPU interconnect technologies.
As organizations seek alternatives to GPU-based infrastructure, Gaudi 2 provides a competitive platform for large-scale distributed AI training workloads and enterprise machine learning deployments.
The next important chip to add is NVIDIA H20.
Why this one next:
Below is the page written in the same structure as your other GPU pages.
