AMD is well known for providing in-depth technical disclosures of its products, often long after their initial launch. At Hot Chips 2024, the company presented a detailed overview of its Instinct MI300X GPU, offering valuable insights into the architecture that powers one of the few non-NVIDIA AI accelerators generating billions in annual revenue. This presentation came just after AMD’s acquisition of ZT Systems, the manufacturer behind Microsoft Azure’s MI300X servers.
💻 Deep Dive into the MI300X Architecture #
The MI300X is part of AMD’s CDNA 3 family, built to power large-scale AI training and inference workloads. While its sibling, the MI300A, is designed for supercomputers like HPE’s El Capitan, the MI300X has become the primary revenue engine for AMD’s data center GPU business—driving over $4 billion this year alone.
At the heart of the MI300X is a multi-chiplet design integrating compute dies, high-bandwidth memory, and interconnect logic. It features an 8-stack HBM3 configuration delivering a massive 192GB of memory and a peak bandwidth of >5 TB/s. Complementing this is a 256MB Infinity Cache, alongside per-core L2 caches that optimize data locality for large AI models.
The compute complex, known as XCDs (Compute Dies), is connected through Infinity Fabric, enabling flexible partitioning across memory and compute domains. This allows the GPU to run as a unified device or as multiple logical partitions, offering scalability for diverse workloads ranging from model training to inference serving.
🧩 Architectural Highlights #
- Process Technology: Advanced multi-chip module built on TSMC’s 5nm and 6nm nodes.
- Memory: 8× HBM3 stacks providing 192GB capacity and up to 5.3TB/s bandwidth.
- Cache System: 256MB Infinity Cache + distributed L2 cache layers for reduced latency.
- Fabric: Infinity Fabric interconnect supporting multi-GPU topologies.
- RAS Features: Hardware-level Reliability, Availability, and Serviceability for hyperscale clusters.
AMD’s 8-way OAM MI300X platform demonstrates the company’s answer to NVIDIA’s HGX systems. Each GPU includes seven high-speed links for peer-to-peer communication and direct host connections, forming the backbone of AMD’s large-scale AI compute nodes.
🔧 Platform and Software Ecosystem #
While the hardware impressed, AMD also emphasized software maturity. Its open-source ROCm stack continues to evolve, supporting popular frameworks like PyTorch and TensorFlow, and adding better kernel optimization for LLM workloads.
AMD’s internal benchmarks suggest that the MI300X can match or even outperform NVIDIA’s H100 in certain AI and HPC workloads. The company also teased upcoming successors—the MI325X (launching later this year) and the MI350 with 288GB of HBM3E, expected in 2025.
📝 Summary #
The Instinct MI300X showcases AMD’s ability to compete head-to-head with NVIDIA in the high-end AI accelerator market. Featuring a massive memory footprint, high compute density, and robust scalability, the MI300X is central to AMD’s growing presence in hyperscale data centers.
With its combination of CDNA 3 architecture, 192GB of HBM3, and continued software ecosystem improvements, AMD has solidified its position as the second-largest player in the AI GPU market, setting the stage for even stronger competition in the years ahead.
