Gaudi 2 vs MI355X

The MI355X outperforms the Gaudi 2 significantly in compute capabilities: 2300 TFLOPS FP16 and FP32 compared to 420 TFLOPS on each for Gaudi 2. This delta translates to over 5 times faster matrix multiplications in deep learning training phases, reducing epoch times for large datasets. FP8 support at 4600 TFLOPS on MI355X further accelerates inference for quantized models, enabling higher throughput in deployment scenarios.

Memory specifications favor the MI355X decisively: 288 GB HBM3e VRAM versus 96 GB HBM2e allows larger batch sizes without gradient accumulation, minimizing overhead in training billion-parameter LLMs. The 8000 GB/s bandwidth on MI355X versus 2460 GB/s on Gaudi 2 supports faster data movement, critical for memory-bound workloads like transformer models where bandwidth bottlenecks limit effective utilization.

Power consumption differs modestly: 750W TDP for MI355X against 600W for Gaudi 2. In real-world clusters, this implies higher density needs for Gaudi 2 but immediate availability aids current deployments. Interconnect choices affect scaling: Ethernet on Gaudi 2 suits standard data centers, while Infinity Fabric on MI355X optimizes low-latency multi-GPU communication.

Select the Gaudi 2 for cost-sensitive projects requiring immediate deployment. With pricing from $0.91 per hour and average $1.08 per hour across two live offers, it provides accessible FP16 performance of 420 TFLOPS without upfront wait times. Its 600W TDP enables denser cloud configurations compared to higher-power alternatives.

Gaudi 2 suits smaller-scale training or inference where 96 GB VRAM suffices, such as fine-tuning models under 70 billion parameters. Ethernet interconnect integrates seamlessly into existing Ethernet-based clusters, avoiding specialized fabric setups.

Choose the MI355X for demanding workloads needing massive scale. Its 288 GB HBM3e VRAM and 8000 GB/s bandwidth handle enormous batch sizes in LLM training, far beyond Gaudi 2's 96 GB and 2460 GB/s limits. FP16 at 2300 TFLOPS accelerates convergence on datasets for models over 100 billion parameters.

Inference-heavy applications benefit from FP8 at 4600 TFLOPS, supporting high-volume quantized serving. Infinity Fabric enhances multi-node efficiency in AMD-optimized environments, ideal for future hyperscale deployments despite current unavailability.