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torch.compile (inductor)

PyTorch · python · MIT

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6_Matmul_with_large_K_dimension_.py
curl "https://kernelindex.com/api/v1/implementations/kernelbench-l1-6-matmul-with-large-k-dimension-torch-compile-inductor?include=source"
interfacepython · torch_compile_inductor
symbolModel.forward
Compatibility
measured onNVIDIA H100
declared hardwaredeclared only
architectures—
dtypes

Benchmark evidence

2 measurements across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
Matmul with large K dimensionfp32 · [256, 524288]
NVIDIA H100
1.38ms±0.01
#2 of 2
2026-03-05
Matmul with large K dimensionfp32 · [256, 524288]
NVIDIA H100
1.83ms±0.01
#2 of 2
2026-03-05

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:840bbec2e40cdb8afd07662a2e36a385b21ae531054ffed365fe7f41bf6d64a5
license declaredMIT
license concludedMIT
imported2026-08-26

Kernel source

6_Matmul_with_large_K_dimension_.py34 lines
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a single matrix multiplication (C = A * B) with a large K dimension
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """
        Performs matrix multiplication of A and B.

        Args:
            A: Input tensor of shape (M, K)
            B: Input tensor of shape (K, N)

        Returns:
            Output tensor of shape (M, N)
        """
        return torch.matmul(A, B)

M = 256
N = 256
K = 131072 * 4

def get_inputs():
    A = torch.rand(M, K)
    B = torch.rand(K, N)
    return [A, B]

def get_init_inputs():
    return []  # No special initialization inputs needed
scrolls · 34 lines total

Source code from KernelBench, © 2023 Anne Ouyang, Simon Guo, Azalia Mirhoseini (Scaling Intelligence Lab, Stanford University), MIT License · MIT

Best evidence level for this revision: reported

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