Posts by Evan Masters
Completing the GPU Performance Picture: Understanding TAF Alongside Peak FLOPs and MAF
- 06 October 2026
Modern AI accelerators can execute an extraordinary number of floating-point operations every second. But a single FLOPs number rarely tells the full performance story. In the first two blogs in this series, we discussed Peak FLOPs and Max-Achievable FLOPs (MAF) for AMD Instinct™ GPUs., we discussed Peak FLOPs and Max-Achievable FLOPs (MAF) for AMD Instinct™ GPUs. Peak FLOPs describe the theoretical compute capability of the hardware. MAF goes a step further by measuring the highest sustained throughput the GPU can achieve after testing a range of GEMM sizes. This blog introduces a third metric: Typical Attained FLOPs (TAF). TAF measures FLOP-weighted sustained throughput across a fixed representative GEMM set selected before testing. The goal is not to replace Peak FLOPs or MAF. Each metric answers a different question, and together they provide a more complete view of GPU compute performance.
Measuring Max-Achievable FLOPs – Part 2
- 28 February 2025
In our previous blog post, we explored the conceptual differences between Peak FLOPs and Max-Achievable FLOPs (MAF), explaining why the gap between these metrics has widened with modern ML-optimized hardware. This second installment provides a detailed methodology for measuring MAF on AMD GPUs, including the specific environmental conditions, matrix size optimization techniques, and tools required for accurate measurement. We present the actual MAF results for AMD Instinct MI300X and MI325X GPUs across different precision formats (FP16, BF16, and FP8) along with their corresponding median frequencies. We also explain how software efficiency and frequency management impact MAF, and demonstrate why boost clock capabilities remain important for latency-sensitive workloads such as LLM inference with small batch sizes.