Have you benchmarked grouped GEMM vs. batched GEMM for your use case? Let’s discuss below ⬇️
📖 NVIDIA cuBLASLt Developer Guide → Grouped GEMM section cublaslt grouped gemm documentation
Enter – a game changer for batched, variable-sized matmul operations. Have you benchmarked grouped GEMM vs
If you're working with (e.g., in LLM inference, attention mechanisms, or recommendation systems), you’ve likely hit the overhead of launching many separate GEMM kernels. in LLM inference
🔍 The grouped GEMM interface allows you to execute a list of independent matrix multiplications in a single kernel launch , drastically reducing launch latency and improving GPU utilization.
#CUDA #cuBLASLt #GPUComputing #GEMM #LLM #PerformanceOptimization Would you like a shorter version for Twitter/X or a code snippet example to accompany this post?