> ## Documentation Index
> Fetch the complete documentation index at: https://bench.flashinfer.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# GEMM

General Matrix Multiplication (GEMM) operation that computes C = A × B^T. This is a fundamental linear algebra operation used in neural networks for layer computations, attention mechanisms, and other matrix transformations.

Variants:

* FP16 GEMM: Uses 16-bit floating point (FP16) inputs for A and B matrices
* FP8 GEMM: Uses 8-bit floating point (FP8) inputs for A and B matrices, with scaling factors to maintain numerical stability

Axes (3 dimensions):

* `M`: variable
* `N`, `K`: constant

Inputs (2 or 4 tensors):

* `A`: \[M, K]
* `B`: \[N, K]
* Scaling factors for FP8 GEMM:
  * `A_scale`: \[M]
  * `B_scale`: \[N]

Outputs (1 tensor):

* `C`: \[M, N]