Intel Gaudi (Habana)
Vendor: Intel Habana
Category: ASIC Dedicated Accelerator
Architecture: Gaudi / Xe HPC
Introduction
Intel Gaudi AI accelerator (formerly Habana Labs), purpose-built for AI training and inference optimization. Gaudi 2 and Gaudi 3 excel at LLM training, support PyTorch and TensorFlow, with competitive pricing.
Specifications
| Model | Compute | Memory | Interface | TDP | Process |
|---|---|---|---|---|---|
| Gaudi 3 | 1,678 TFLOPS (FP8) / 1,678 (BF16) | 128GB HBM2e (3.7 TB/s) | OAM + 24x 200GbE | 900W | 5nm |
| Gaudi 2 | 865 TFLOPS (FP8) / 432 (BF16) | 96GB HBM2e (2.45 TB/s) | OAM + 24x 100GbE | 600W | 7nm |
Official Website
Driver Downloads
Linux
Related Documentation
- Habana Documentation Center
- Gaudi Installation Guide
- PyTorch Adapter (HPU)
- Gaudi Performance Optimization
OS Support
| Windows | Linux | macOS | Android |
|---|---|---|---|
| ❌ | ✅ | ❌ | ❌ |
Version History
| Version | Release Date | Description |
|---|---|---|
| SynapseAI 1.18 | 2025-Q1 | Full Gaudi 3 support |
| SynapseAI 1.16 | 2024-Q2 | PyTorch 2.1 native integration |
Performance Benchmarks
| Model | Task | Performance Metric |
|---|---|---|
| Gaudi 3 × 8 | GPT-3 175B Training | ~1.2 days (estimated) |
| Gaudi 3 | Llama 2 70B Inference | ~100 tok/s (FP8) |
| Gaudi 2 × 8 | BERT-Large Training | ~90% H100 efficiency |
Pricing
| Model | Reference Price | Notes |
|---|---|---|
| Gaudi 3 | Contact vendor | Available via Intel Developer Cloud or ODMs |
| Gaudi 2 | $8,000-12,000 | Market price below H100 |
Quick Installation
Linux (Ubuntu 22.04)
# 1. Install Habana driver
sudo ./habana-driver-*.run --install
# 2. Install SynapseAI SDK
sudo ./SynapseAI-*.run --install
# 3. Verify
hl-smi
Gaudi 2 can also be trialled for free via Intel Developer Cloud.
Code Examples
Python (PyTorch on HPU)
import torch
import habana_frameworks.torch.core as htcore
# Use HPU backend (API consistent with CUDA)
device = torch.device("hpu")
x = torch.randn(1024, 1024, device=device)
y = torch.matmul(x, x)
print(f"HPU matrix multiply: {y.shape}")
# Habana Graph mode (similar to CUDA Graphs)
htcore.hpu.graphs()
Architecture Highlights
- Gaudi Architecture: AI accelerator purpose-built for large model training, integrated 24x RoCE 2.0 network interfaces, supporting direct large-cluster interconnect
- SynapseAI: Intel AI software stack, native PyTorch/TensorFlow support
- Competitive Pricing: Gaudi 2 priced at roughly 1/3 of H100, suitable for budget-constrained training scenarios
Model Compatibility
| Model/Framework | Support | Notes |
|---|---|---|
| PyTorch | ✅ Native | HPU backend, CUDA-API compatible |
| TensorFlow | ✅ | Habana backend |
| JAX | ⚠️ | Experimental support |
| Llama / Qwen and similar LLMs | ✅ | DeepSpeed / FSDP both supported |
| Stable Diffusion | ✅ | HPU backend |
Large-Scale Cluster Deployments
Based on global AI supercomputing cluster statistics, Intel Gaudi has accumulated over 4,000 chips deployed across 1 publicly disclosed clusters.
Chip Model Statistics
| Chip Model | Total Deployed | Cluster Count |
|---|---|---|
| Intel Habana Gaudi2 | 4,000 | 1 |
Notable Deployment Clusters Top 10
| # | Cluster Name | Total Chips | Chip Model | Operator |
|---|---|---|---|---|
| 1 | Intel Stability Gaudi 2 | 4,000 | Intel Habana Gaudi2 ×4,000 | Intel, United States of America |
Related Products
If you're evaluating alternatives, the following products may also fit your scenario:
- NVIDIA GPU / CUDA — NVIDIA (GPU Graphics Processor)
- Google Cloud TPU — Google (TPU Tensor Processor)
- AWS Trainium2 / Inferentia2 — Amazon AWS (ASIC Dedicated Accelerator)
- AMD ROCm / GPU — AMD (GPU Graphics Processor)
- Cerebras WSE-3 — Cerebras (ASIC Dedicated Accelerator)
- SambaNova RDU — SambaNova (ASIC Dedicated Accelerator)
- Cambricon Siyuan MLU — Cambricon (ASIC Dedicated Accelerator)