Customers evaluating AI infrastructure increasingly rely on both industry‑standard benchmarks and real‑world model performance—such as MLPerf Inference, Llama 3.1 405B, and DeepSeek‑R1—to guide their GPU purchase decisions. AMD’s strategy balances peak benchmark results with Day 0 support and rapid model tuning to meet production workloads.
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MLPerf Inference 5.0: A Series of Firsts
In October 2024, AMD launched its Instinct MI325X GPU and submitted its first MLPerf Inference 5.0 numbers on this hardware. Key milestones included:
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First-ever MI325X submission of MLPerf Inference results
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First multi‑node submission in collaboration with a partner
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Enabling multiple partners (Supermicro, ASUS, Gigabyte, MangoBoost) to publish their own MI325X results
Partner Submissions and Scalability
Multiple ecosystem partners demonstrated performance parity with AMD’s in‑house Llama 2 70B and Stable Diffusion XL submissions, underscoring platform consistency. Notably, MangoBoost set a new MLPerf offline record for Llama 2 70B using four Instinct MI300X nodes, validating the scalability of AMD’s multi‑node solutions.
ROCm Software Innovations
Strong MLPerf results arise from the synergy between MI325X hardware (2.048 TB HBM3e per node, 6 TB/s bandwidth) and continuous ROCm enhancements:
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Bi‑weekly ROCm container updates (kernel scheduling, GEMM tuning, inference efficiency)
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Quark tool enabling FP16→FP8 quantization
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vLLM and memory‑handling optimizations
These advancements unlock the full potential of AMD Instinct GPUs.
Leadership on Leading Open‑Source Models
AMD extends its momentum beyond benchmarks to real‑world models:
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DeepSeek‑R1: MI300X achieved a 4× inference speedup in just 14 days, rivaling NVIDIA H200.
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Llama 3.1 405B: Optimized for MI300X, AMD became the exclusive inference solution for Meta’s frontier model, outperforming NVIDIA H100 on memory‑bound workloads.
Advanced Ecosystem Tools
To further empower customers, AMD introduced:
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AI Tensor Engine for ROCm (AITER): Up to 17× faster decoder execution, 14× Multi‑Head Attention, 2× LLM inference throughput.
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Open Performance and Efficiency Architecture (OPEA): Cross‑platform telemetry integrated with PyTorch, Triton, and multi‑GPU setups.
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GPU Operator for Kubernetes: Enhanced multi‑instance GPU support and tighter ROCm integration for production AI.
Commitment to Transparency
AMD publishes all ROCm container recipes, full MLPerf submission data on MLCommons, and open‑source artifacts on GitHub. This transparency ensures customers can reproduce results and trust AMD’s performance claims.
