AMD Instinct MI325X (8-GPU platform)

The AMD Instinct MI325X 8-GPU platform is a purpose-built accelerator baseboard integrating eight MI325X GPUs interconnected via AMD Infinity Fabric, designed to address the extreme memory and compute demands of frontier-scale AI workloads. Each MI325X GPU is built on the CDNA3 architecture and features 288GB of HBM3E memory, yielding a combined 2.304TB of high-bandwidth memory across the full eight-GPU configuration—sufficient to hold trillion-parameter large language models entirely in accelerator memory without relying on CPU-side offloading. The platform delivers aggregate peak AI compute exceeding 5,200 TFLOPS in FP8 precision, making it one of the highest-density inference and training solutions available in a single baseboard form factor.

The CDNA3 architecture underpinning each MI325X GPU introduces second-generation matrix cores with native FP8, BF16, FP16, and FP32 support, enabling flexible mixed-precision workflows across training, fine-tuning, and high-throughput inference. AMD Infinity Fabric links all eight dies at high bandwidth, enabling coherent memory access and efficient collective communications across the full GPU ensemble without the latency penalties associated with PCIe-only multi-GPU topologies. The platform is compatible with ROCm open software stack, supporting PyTorch, TensorFlow, JAX, and the vLLM inference engine through validated HIP kernels and MIOpen libraries.

Launched in Q1 2025, the AMD Instinct MI325X 8-GPU platform targets hyperscale data centers and enterprise AI infrastructure teams deploying large language models, diffusion models, and scientific simulation workloads that exceed the memory envelope of single-socket or dual-GPU configurations. The platform integrates into server chassis supporting the OAM (OCP Accelerator Module) form factor, enabling rack-scale deployment alongside high-speed Ethernet or InfiniBand networking fabrics. Its 2TB+ aggregate HBM3E capacity eliminates model sharding constraints for models in the 500B–1T+ parameter range, dramatically simplifying deployment orchestration and reducing end-to-end inference latency.

Ideal for

• Training and fine-tuning trillion-parameter large language models in-memory without parameter offloading to CPU or NVMe tiers
• High-throughput LLM inference serving for enterprise generative AI applications requiring sub-100ms token generation latency at scale
• Multi-modal foundation model development combining large vision encoders and language decoders within a unified high-bandwidth memory pool
• Genomics and computational biology workloads processing petabyte-scale sequence datasets requiring sustained memory bandwidth beyond CPU DRAM limits
• Climate and physics simulation at continental scale leveraging FP64 and mixed-precision tensor operations across the full eight-GPU collective
• Retrieval-augmented generation (RAG) pipelines with in-accelerator vector store caching to minimize round-trip latency for enterprise knowledge bases

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.