AMD Instinct MI325X Accelerator

The AMD Instinct MI325X is a data center GPU accelerator built on AMD's CDNA 3 architecture, featuring 256GB of HBM3E high-bandwidth memory — the largest memory capacity available in a production AI accelerator at its launch in Q4 2024. This exceptional memory footprint enables the accelerator to hold extremely large model parameter sets entirely in-device memory, eliminating costly host-to-device data transfers during inference and enabling full-scale training of multi-hundred-billion parameter large language models without model parallelism across additional nodes. The MI325X delivers 6.0 TB/s of aggregate memory bandwidth, providing the throughput necessary to feed its compute engines during memory-bound inference workloads at high batch sizes.

The CDNA 3 architecture underlying the MI325X features a chiplet-based design combining compute dies with HBM3E memory stacks, interconnected through AMD's advanced packaging technology. The architecture includes second-generation AMD Matrix Cores optimized for mixed-precision AI workloads, supporting FP8, FP16, BF16, and FP32 data formats natively. With 1,307 TFLOPS of FP16 peak throughput and 2,615 TOPS of FP8 performance, the accelerator scales efficiently across transformer-based model architectures commonly used in generative AI. AMD Infinity Fabric interconnects deliver 896 GB/s of bidirectional bandwidth for multi-GPU scaling within a node.

Designed for enterprise hyperscale and cloud deployments, the MI325X integrates into AMD Instinct platform-compatible OCP Accelerator Module (OAM) and PCIe form factors, supporting ROCm open-source software stack compatibility. The accelerator targets AI infrastructure operators who require maximum per-accelerator memory to reduce the total number of accelerators needed for large model deployment, directly improving total infrastructure efficiency. Security features include secure boot and hardware-level isolation appropriate for multi-tenant cloud environments.

Ideal for

• Training frontier-scale large language models with hundreds of billions of parameters, leveraging 256GB on-device memory to reduce inter-node communication overhead
• High-throughput generative AI inference serving for production LLM APIs, where large KV cache requirements demand maximum memory capacity per accelerator
• Multimodal AI model development combining vision transformers and language models that benefit from unified large memory pools
• Scientific simulation and HPC workloads requiring both high double-precision floating-point performance and massive memory capacity for in-memory dataset processing
• Enterprise retrieval-augmented generation (RAG) pipelines where embedding models and generative models must co-reside in accelerator memory for latency-critical applications
• AI model fine-tuning and continual learning pipelines in regulated industries where data residency requires on-premises GPU clusters with maximum per-node capacity

Technical specifications

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