Alex Medick

AMD’s MI325X crushes MLPerf benchmarks with top-tier LLM and SDXL performance. See why TensorWave is betting big on open, memory-rich AI compute

How the MI325X Became the Ultimate AI Performance Benchmark

In AI, speed isn't just a luxury—it’s a necessity. And this week, AMD's Instinct™ MI325X GPUs proved they can go toe-to-toe with the best, delivering industry-leading results in the latest MLPerf Inference v5.0 benchmarks. Whether you're running massive LLMs or generating high-res images with Stable Diffusion XL, the MI325X is showing up strong—and we’re excited about what that means for our customers at TensorWave.


MI325X: Built for Serious Inference

Before diving into the benchmarks, here’s

In AI, speed isn't just a luxury—it’s a necessity. And this week, AMD's Instinct™ MI325X GPUs proved they can go toe-to-toe with the best, delivering industry-leading results in the latest MLPerf Inference v5.0 benchmarks. Whether you're running massive LLMs or generating high-res images with Stable Diffusion XL, the MI325X is showing up strong—and we’re excited about what that means for our customers at TensorWave.


MI325X: Built for Serious Inference

Before diving into the benchmarks, here’s what gives the MI325X its edge:

 * 256GB HBM3e per GPU (with 6TB/s bandwidth) = room for massive models without needing massive clusters.
 * Optimized for multiple precisions (FP16, BF16, FP8, INT8), which is critical for real-world workloads.
 * No CUDA tax—this is open-source-first, ROCm-native performance.


MLPerf Results: Llama 2 and Stable Diffusion XL (SDXL) Put to the Test

AMD submitted two heavy workloads to MLPerf:

 * Llama 2 70B inference
 * SDXL (text-to-image generation)

Both ran on MI300X hardware, with optimized software stacks built for real-world throughput.

🦙 Llama 2 70B

 * AMD used vLLM + Quark for quantization (FP8-e4m3), and optimized scheduling to keep GPUs fed.
 * GEMM tuning pushed TFLOPs past 1.5K per GPU.
 * MI325X delivered inference performance competitive with Nvidia’s H200, with fewer GPUs and better memory utilization.

🎨 Stable Diffusion XL

 * Leveraged SHARK + IREE compiler stacks for deep graph-level optimizations.
 * Post-training quantization focused on the UNet bottleneck using Brevitas (INT8 for most ops, FP8 for attention).
 * Batching strategies pushed 1,024 image generations simultaneously in offline mode.


Why It Matters for You

At TensorWave, we’re already running MI300X at scale and building out our MI325X capabilities. These results confirm what our users are seeing with AMD Instinct MI Series GPUs:

 * Higher throughput per dollar
 * Lower latency for large-context inference
 * Room to run bigger models without playing memory Tetris

Whether you're scaling LLM inference or deploying high-load image generation apps, MI325X on TensorWave gives you the performance you need—without vendor lock-in or CUDA tax.

Closing:AI infrastructure shouldn’t be the bottleneck to your innovation. With MI325X and these MLPerf results in hand, we’re doubling down on what we already believe:The future of AI compute is open, memory-rich, and lightning fast.

Ready to experience it? Deploy today on TensorWave!


About TensorWave

TensorWave is the AI and HPC cloud purpose-built for performance. Powered exclusively by AMD Instinct™ Series GPUs, we deliver high-bandwidth, memory-optimized infrastructure that scales with your most demanding models—training or inference.

AMD Instinct™ MI355X GPUs Now Available

How the MI325X Became the Ultimate AI Performance Benchmark

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