AMD Instinct™ GPUs offer:

• Exceptional memory bandwidth for AI training
• Optimized matrix operations for deep learning models
• Cost-effective scaling for cloud AI infrastructure
• Competitive performance per watt in HPC environments

These accelerators excel in:

• AI training & inference (Deep learning, Large Language Models)
• High-performance computing (HPC) (Scientific simulations, complex computations)
• Data analytics & machine learning
• Cloud-based AI applications

The MI300X features HBM3 (High-Bandwidth Memory), which allows faster data processing and reduces bottlenecks in AI training. This is crucial for efficiently handling large datasets and running multi-trillion-parameter AI models.

These GPUs are designed with high-performance per watt efficiency, balancing energy consumption with top-tier AI processing capabilities. Exact power usage varies depending on workload intensity and data throughput.

AMD Instinct™ GPUs enhance AI model training speed through a combination of optimized AI compute cores, which efficiently handle matrix and tensor operations, and high FP16/FP32 performance that accelerates complex computations. The MI325X, equipped with HBM3E memory, further boosts performance by enabling low-latency, high-speed data transfer, making it ideal for demanding AI workloads.

High-speed tensor operations on AMD Instinct™ GPUs accelerate the training of open-source models like LLaMA and other large language models. With HBM3 memory, these GPUs enable seamless processing of multi-trillion-parameter models, while cloud-native support ensures efficient scaling of distributed AI workloads.

The MI325X delivers a notable performance uplift over the MI300X, especially in large-scale AI training. It features faster HBM3E memory (up to 6 TB/s bandwidth) and improved compute throughput, making it better suited for training massive models like GPT-style transformers. The MI325X is architected to provide more sustained performance across more extended workloads with better efficiency.

The MI325X improves upon the MI300X with faster memory bandwidth (HBM3E vs. HBM3), greater total memory capacity (up to 288 GB HBM), and higher peak FP8/FP16 performance. These enhancements directly translate to better throughput and scalability for deep learning, particularly for training and serving large foundation models across multi-GPU clusters.

MI325X GPUs are built for scale. They enable efficient distributed training of large language models with high-speed interconnects, increased memory bandwidth, and optimized support for multi-GPU and multi-node configurations. The MI325X’s design helps reduce communication bottlenecks and maximizes compute utilization across large clusters.