Cublaslt Grouped Gemm Jun 2026

In the world of High-Performance Computing (HPC) and Deep Learning (DL), the General Matrix Multiply (GEMM) operation is the undisputed king. From large language models (LLMs) to scientific simulations, performance often hinges on how efficiently you can compute C = α*A*B + β*C .

While standard grouped GEMMs in NVIDIA Docs improve efficiency by launching multiple operations in one kernel, they often suffer from when one large GEMM is grouped with many tiny ones. A load-balanced scheduler would dynamically reassign thread blocks across the GPU to ensure that all Streaming Multiprocessors (SMs) finish their work at roughly the same time. Why this is a "Good Feature": cublaslt grouped gemm

In modern deep learning and HPC applications, workload shapes are rarely uniform. Standard approaches often struggle with these "ragged" batches: In the world of High-Performance Computing (HPC) and

Supports combinations like , BF16 , and FP16 with high-throughput Tensor Core acceleration. Fused Epilogues Fused Epilogues Unlike legacy cublasGemmStridedBatchedEx

Unlike legacy cublasGemmStridedBatchedEx , which requires all matrices in a batch to have the , cuBLASLt Grouped GEMM supports variable dimensions per group.

Here is a deep dive into cublasLtMatmul with grouped GEMM functionality.