MI250X vs MI325X

The MI325X delivers 1307 TFLOPS in FP16 and FP32, a 3.4 times increase over the MI250X's 383 TFLOPS, accelerating deep learning training cycles significantly. For inference, the MI325X adds FP8 support at 2614 TFLOPS, enabling quantized models to process more tokens per second in latency-sensitive applications. This FP16 and FP32 parity on both GPUs suits mixed-precision training without format conversion penalties.

Memory specifications transform real-world usage: 256 GB HBM3e on the MI325X supports models exceeding 128 GB, such as massive LLMs, while 6000 GB/s bandwidth sustains larger batch sizes in training, reducing per-iteration time by minimizing data stalls. The MI250X's 3277 GB/s limits it to smaller batches or models under memory pressure. Higher TDP on the MI325X at 750 W versus 560 W reflects denser compute but demands robust cooling.

These differences position the MI325X for frontier workloads, whereas the MI250X handles established pipelines efficiently.

The MI250X suits immediate deployments requiring availability and cost efficiency. With pricing from $1.28 per hour and an average of $1.46 per hour across four live cloud offers, it avoids delays from the MI325X's lack of current availability. Its 560 W TDP consumes less power, benefiting constrained data centers or prolonged runs.

Budget-sensitive projects with models fitting 128 GB HBM2e, such as fine-tuning mid-sized LLMs, favor the MI250X over waiting for newer hardware.

The MI325X excels in demanding AI tasks leveraging its superior specifications. Deploy it for training LLMs needing over 128 GB VRAM or inference with 6000 GB/s bandwidth to maximize throughput on large batches. FP8 at 2614 TFLOPS optimizes quantized serving for production-scale models.

Future-oriented users prioritize the MI325X for CDNA 3 advancements, accepting 750 W TDP for 3.4 times FP16 performance gains once offers appear.