Supermicro A+ Server AS-8125GS-TNMR2 8U GPU Server with AMD Instinct MI300X 8-GPU

The Supermicro AS-8125GS-TNMR2 is an 8U rackmount server platform engineered around eight AMD Instinct MI300X OAM (Open Accelerator Module) GPUs, delivering a unified compute-and-memory architecture purpose-built for large-scale AI training, inference, and HPC workloads. Each MI300X accelerator integrates 192 GB of HBM3 memory with 5.3 TB/s of aggregate memory bandwidth per GPU, and the eight-GPU configuration provides a total of 1,536 GB of pooled GPU memory within a single server node—enabling models and datasets that would otherwise require multi-node distribution to reside entirely in-node.

The MI300X is built on AMD's CDNA 3 GPU architecture, combining CPU-class compute dies with stacked HBM3 memory in a single package. Each GPU delivers up to 1,307 TFLOPS of FP8 peak throughput and 653 TFLOPS of FP16, making the platform well-suited for generative AI inference at scale, large language model (LLM) fine-tuning, and dense matrix workloads. The Supermicro AS-8125GS-TNMR2 server chassis provides the thermal, power, and interconnect infrastructure required to sustain eight MI300X OAMs under full load, including high-bandwidth NVLink-class peer-to-peer GPU communication through AMD's Infinity Fabric interconnect.

Designed for enterprise datacenters and hyperscale AI infrastructure, the AS-8125GS-TNMR2 supports AMD ROCm open software platform compatibility, enabling integration with popular AI frameworks including PyTorch, TensorFlow, and JAX. The platform is relevant to organizations in financial services, healthcare AI, national research computing, telecommunications, and cloud service providers deploying inference endpoints or training clusters at scale. Omnixon Global supplies this platform to enterprise and institutional buyers across the UAE, GCC, EMEA, and APAC regions.

Ideal for

• Large language model (LLM) inference serving, where the 1,536 GB total GPU memory pool enables full in-node hosting of 70B–400B parameter models without tensor sharding across nodes
• Generative AI model fine-tuning and instruction tuning for enterprise-customized foundation models in regulated industries such as finance and healthcare
• High-performance scientific computing and simulation workloads including molecular dynamics, climate modeling, and seismic processing that require sustained FP64 throughput
• AI-driven drug discovery and genomics pipelines requiring large memory capacity for simultaneous processing of high-dimensional biological datasets
• Enterprise AI inference consolidation, replacing multi-node GPU clusters with a single 8U server node to reduce rack footprint, interconnect complexity, and operational overhead
• Sovereign AI and national research computing deployments where a single dense node must support multiple concurrent research teams running independent large-model workloads

Technical specifications

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