GIGABYTE G383-R80 4-GPU Server (AMD Instinct MI300A)

The GIGABYTE G383-R80 is a 3U rackmount server engineered around the AMD Instinct MI300A Accelerated Processing Unit, which uniquely integrates CPU compute cores (AMD EPYC 'Genoa' architecture) and CDNA3 GPU compute dies onto a single package sharing a unified HBM3 memory pool. This heterogeneous architecture eliminates the traditional CPU-to-GPU PCIe memory transfer bottleneck, enabling the processor and accelerator to operate directly on the same physical memory space — a fundamental advantage for memory-bandwidth-intensive HPC and AI inference workloads. The G383-R80 accommodates up to four MI300A APUs, delivering substantial aggregate HBM3 capacity and compute throughput in a dense 3U form factor suitable for data center deployment.

Each MI300A APU integrates 24 AMD EPYC CPU cores alongside CDNA3 GPU compute units, with 128 GB of HBM3 memory per APU providing high bandwidth access shared coherently between the CPU and GPU compute elements. The unified memory architecture is particularly beneficial for large language model inference, scientific simulation, and financial analytics, where data sets frequently exceed the available VRAM on discrete GPU solutions. With four APUs installed, the G383-R80 presents a combined 512 GB of HBM3 across the system, positioning it as a high-capacity alternative for workloads that demand both large memory footprints and parallel floating-point throughput.

The G383-R80 targets enterprise HPC centers, national laboratories, cloud infrastructure operators, and AI research organizations seeking an alternative to NVIDIA-based accelerator platforms. The system supports AMD ROCm open software stack compatibility, enabling deployment of frameworks such as PyTorch, TensorFlow, and JAX alongside HPC toolchains including OpenMP and MPI. Its 3U chassis design balances compute density with thermal headroom, incorporating appropriate high-performance cooling infrastructure to sustain the thermal output of four MI300A APUs under sustained workloads.

Ideal for

• Large language model inference serving requiring high HBM capacity to hold full model weights without offloading
• Scientific HPC simulation workloads in computational fluid dynamics, molecular dynamics, and climate modeling benefiting from unified CPU-GPU memory coherency
• AI training runs for mid-scale deep learning models where large unified memory reduces data pipeline complexity
• Financial risk analytics and Monte Carlo simulation requiring high sustained memory bandwidth and parallel floating-point throughput
• Seismic processing and geophysical data analysis in energy sector HPC clusters demanding dense accelerated compute per rack unit
• Government and research laboratory supercomputing nodes requiring open-software-stack accelerators with AMD ROCm ecosystem compatibility

Technical specifications

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