ggml_crossprod

Computes <code>t(A) %*% B</code> (or <code>t(A) %*% A</code> when <code>B</code> is
<code>NULL</code>) on the Vulkan GPU, returning an R matrix, with a CPU fallback.
See <code>ggml_matmul</code> for the precision and dispatch notes.

Provides 'R' bindings to the 'GGML' tensor library for machine
learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU
fallback. The package features a 'Keras'-like sequential API and a
'PyTorch'-style 'autograd' engine for building, training, and deploying
neural networks. Key capabilities include high-performance 5D tensor
operations, 'f16' precision, and efficient quantization. It supports
native 'ONNX' model import (50+ operators) and 'GGUF' weight loading
from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for
zero-overhead inference via dedicated weight buffering, it integrates
seamlessly as a 'parsnip' engine for 'tidymodels' and provides
first-class learners for the 'mlr3' framework.
See <https://github.com/ggml-org/ggml> for more information about the
underlying library.

Yuri Baramykov

ggmlR

'GGML' Tensor Operations for Machine Learning

ggml_crossprod function

<dl><dt>A</dt>
<dd>A numeric matrix.</dd>
<dt>B</dt>
<dd>A numeric matrix with <code>nrow(B) == nrow(A)</code>, or <code>NULL</code>
(default) for <code>t(A) %*% A</code>.</dd>
<dt>device, prec</dt>
<dd>See <code>ggml_matmul</code>.</dd></dl>

Arguments

GPU cross product (drop-in for crossprod) — ggml_crossprod

<dl>

<dt>A</dt>
<dd>A numeric matrix.</dd>


<dt>B</dt>
<dd>A numeric matrix with <code>nrow(B) == nrow(A)</code>, or <code>NULL</code>
(default) for <code>t(A) %*% A</code>.</dd>


<dt>device, prec</dt>
<dd>See <code>ggml_matmul</code>.</dd>

</dl>

ggml_crossprod: GPU cross product (drop-in for `crossprod`)

Description

Usage

Value

Arguments