Characterizing Performance, Power, and Energy of AMD CDNA3 GPU Family
A Comprehensive Third-Party Evaluation of MI300X and MI325X GPUs for HPC and Generative AI Workloads
AMD's CDNA3 architecture, featuring MI300X and MI325X GPUs, delivers significant performance for HPC and generative AI. While MI325X offers higher raw performance and HBM3E bandwidth, MI300X demonstrates better energy efficiency at lower power limits. Key findings include CU-level performance parity, superior matrix core throughput, and memory bandwidth advantages for MI325X, especially when power-capped to match MI300X. Communication links show high theoretical bandwidth utilization, with an emphasis on optimization for multi-GPU applications.
Executive Impact: Key Findings at a Glance
Our in-depth analysis reveals critical performance metrics for the CDNA3 GPU family, underscoring their potential for your most demanding workloads.
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Peak FP32 Perf
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HBM3E BW Increase
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CU-Level Peak Throughput
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding the raw processing capabilities of the CDNA3 GPUs, from vector ALUs to matrix cores, and their device-wide scalability.
Compute Unit Operations Flow
The processing flow within a single Compute Unit illustrates how vector and matrix operations are handled.
Operand Gathering
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SIMD Unit (Vector ALUs)
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Matrix Cores
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Special Functional Units
Vector ALU FP32 Peak Throughput
Vector-vector fused-multiply-accumulate (FMA) operations achieve near-theoretical peak throughput in FP32 on CDNA3.
99% Achieved Throughput of PeakMatrix Core INT8 Peak Throughput
Matrix-multiply-accumulate operations in INT8 achieve maximal efficiency across all supported data types.
99% Achieved Throughput of PeakAnalysis of on-chip and off-chip memory bandwidth and latency, alongside inter-device communication performance.
| Feature | MI300X (HBM3) | MI325X (HBM3E) |
|---|---|---|
| Capacity | 192 GB | 256 GB |
| Average Bandwidth | 4.057 TB/s | 4.710 TB/s (+16.1%) |
| Memory Clock | 1300 MHz | 1500 MHz |
| On-chip Latency (L1V/L2/LLC) | Identical | Identical |
CPU-GPU Bidirectional Bandwidth
Achieved bidirectional bandwidth between CPU and GPU over PCIe 5.0 x16 links.
96 PCIe Bidirectional Bandwidth GB/sPerformance, power, and energy efficiency evaluation across a suite of HPC and AI workloads including GEMM, HPL, HPCG, GROMACS, and LLM inference.
GEMM/GEMV Performance Insights
For General Matrix-Matrix Multiplication (GEMM) and General Matrix-Vector Multiplication (GEMV), the full-power MI325X achieves the highest throughput. However, the power-capped MI325X (at 750W, matching MI300X's TDP) often provides the best energy efficiency and improved throughput compared to MI300X, solely due to its superior HBM3E memory bandwidth.
Metric: Throughput
Value: 28% higher
Context: MI325X vs MI300X in fp64 GEMM
HPCG & Memory Bound Workloads
HPCG, being a memory-bound application, benefits significantly from higher memory bandwidth. The MI325XM (power-capped MI325X with manual clock frequency tuning to prioritize HBM3E bandwidth) shows the best energy efficiency and strong throughput due to its HBM3E advantage, even when operating at the same power limit as MI300X.
Metric: Energy Efficiency (HPCG)
Value: Best
Context: MI325XM (Power-capped) for HPCG
LLM Inference Serving
In LLM inference serving for models like DeepSeek R1, the full-power MI325X provides the highest throughput and lowest end-to-end latency. However, for optimal energy efficiency, the power-capped MI325X proves to be the superior choice, demonstrating the trade-offs between raw performance and sustainable operation for large-scale AI deployments.
Metric: Energy Efficiency (LLM)
Value: Best
Context: MI325X (Power-capped) for LLM Inference
Advanced ROI Calculator: Accelerate Your AI Initiatives
Estimate your potential annual savings and reclaimed human hours by deploying CDNA3 GPUs for your enterprise AI workloads. Adjust parameters to see the impact.
Potential Annual Savings
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Reclaimed Human Hours Annually
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Our AI Implementation Roadmap
A clear path to integrating CDNA3 GPUs into your enterprise infrastructure, from initial strategy to continuous support.
Discovery & Strategy
Initial consultation, workload assessment, and tailored solution design (1-2 Weeks).
Infrastructure Deployment
Setting up hardware, software, and networking for optimal performance (2-4 Weeks).
Integration & Optimization
Integrating CDNA3 with existing systems and fine-tuning for peak efficiency (3-6 Weeks).
Training & Rollout
User training and full-scale deployment across your enterprise (1-2 Weeks).
Continuous Support
Ongoing monitoring, maintenance, and performance enhancements (Ongoing).
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