AMD MI300x GPUs with GEMM tuning improves throughput and latency by up to 7.2x
Nscale explores AI model optimization through GEMM tuning, leveraging rocBLAS and hipBLASlt for AMD MI300x GPUs. Results show up to 7.2x throughput increase and reduced latency, benefiting large models and enhancing processing efficiency.
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Nscale's recent technical exploration delves into AI model optimization through GEMM tuning, focusing on enhancing throughput and reducing latency. By leveraging libraries like rocBLAS and hipBLASlt, developers can fine-tune applications for improved performance on AMD MI300x GPUs. GEMM tuning involves selecting efficient matrix multiplication algorithms based on hardware characteristics, adjusting parameters, and configuring kernels to optimize workload distribution. Benchmarking tests showcased up to a 7.2x increase in throughput with GEMM tuning, particularly benefiting larger models like LLaMA-2-70B and LLaMA-3-70B. Moreover, latency reductions were observed across models and batch sizes, emphasizing the impact of GEMM tuning on processing efficiency. These findings underscore the significance of GEMM tuning in maximizing AI model capabilities on AMD GPUs, enabling superior performance for complex workloads. The study highlights the critical role of advanced tuning techniques in unlocking hardware potential and ensuring efficient processing for demanding AI tasks.
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