MI250X 128GB

The AMD Instinct MI250X is a dual-die CDNA 2 data center GPU released in November 2021, built on TSMC's 6nm process with 58 billion transistors across two Aldebaran compute dies. Each die contributes 64GB of HBM2e memory, yielding 128GB total — the highest VRAM capacity of any GPU available at its launch and still among the largest in cloud deployments today. The dual-die architecture communicates via AMD's Infinity Fabric interconnect, delivering 3,277 GB/s of aggregate memory bandwidth across both dies.

Compute performance is 383 TFLOPS at FP16 and BF16 — the two precision formats most relevant for transformer model training and inference. FP64 throughput is 47.9 TFLOPS, making the MI250X one of the strongest GPUs for HPC workloads that require double-precision accuracy, such as molecular dynamics, climate simulation, and computational fluid dynamics. Unlike NVIDIA's Hopper generation, the MI250X does not support FP8 precision, which limits its inference efficiency for quantized LLM serving compared to H100 or H200.

The 128GB HBM2e capacity is the MI250X's defining advantage for large model workloads. At FP16 (2 bytes per parameter), the MI250X can hold models up to approximately 64B parameters in a single card — sufficient for Llama 3 70B with room for KV cache. At BF16, the same 64B ceiling applies. INT8 quantization doubles this to ~128B parameters per card, enabling single-card inference for models that would require two A100 80GB cards. The 3,277 GB/s bandwidth is higher than the A100 SXM4 (2,039 GB/s) and comparable to the H100 SXM5 (3,350 GB/s), making the MI250X competitive for memory-bandwidth-bound autoregressive inference.

The dual-die design introduces a NUMA (Non-Uniform Memory Access) topology that software must explicitly handle. Each die has its own 64GB memory pool; cross-die memory accesses incur higher latency than intra-die accesses. Frameworks like PyTorch and ROCm-aware runtimes can manage this automatically, but naive code that treats the 128GB as a flat pool may see performance degradation from cross-die traffic. For model-parallel workloads that split layers across both dies, the Infinity Fabric bandwidth is sufficient, but tensor-parallel configurations should be benchmarked carefully. AWS deploys the MI250X in p4de.24xlarge instances (8× MI250X per node), where the Infinity Fabric topology is exposed through the ROCm NUMA API.

Software compatibility is the primary consideration when evaluating the MI250X. AMD's ROCm stack supports PyTorch, TensorFlow, and JAX, and major inference frameworks including vLLM and llama.cpp have ROCm backends. However, CUDA-native libraries — FlashAttention 2, certain quantization kernels, and TensorRT — require porting or have limited ROCm support. For teams already invested in CUDA toolchains, the migration cost is non-trivial. For HPC workloads using standard MPI/OpenMP patterns, ROCm is a mature and well-supported environment. The MI250X is the predecessor to the MI300X (CDNA 3, 192GB HBM3), which offers significantly higher FP16 throughput and FP8 support — teams evaluating AMD hardware for new deployments should compare both generations.