Machine supercharges your GitHub Workflows with seamless GPU acceleration. Say goodbye to the tedious overhead of managing GPU runners and hello to streamlined efficiency.

What GPU types are available?

Machine offers a wide selection of NVIDIA GPUs including T4G, T4, L4, A10G, and L40S, as well as AWS Inferentia options to match your specific workflow needs.

How do I integrate Machine with GitHub Actions?

Machine integrates natively with GitHub Actions. Simply update your workflows with a runs-on tag and start accelerating your tasks immediately.

How much faster are GPU-accelerated workflows?

Machine accelerates model training, inference, batch processing, and simulations—up to 100× faster than CPU-only workflows.

GitHub Actions GPU Runners | NVIDIA GPU Specifications

Machine provides a variety of GPU runners for GitHub Actions to match your specific CI/CD and machine learning workload requirements. Each GitHub Actions GPU runner type comes with pre-installed NVIDIA Device Drivers 555.58, CUDA 12.1.0 and cuDNN 9.2.1, allowing you to start using GPU acceleration immediately without any configuration.

You are always free to install additional drivers, CUDA, or cuDNN versions, or even build your own from source.

GitHub Actions GPU Runners - Available Types

Machine supports all GPU instances currently available on AWS, including NVIDIA GPUs and AWS Inferentia accelerators.

NVIDIA GPU Runners for GitHub Actions

GPU Type	GPU Memory	CUDA Cores	Tensor Cores	Use Cases
T4G	16GB	2,560	320	Entry-level ML training, inference
T4	16GB	2,560	320	General-purpose ML, computer vision
L4	24GB	7,680	240	Balanced training/inference, mid-range ML
A10G	24GB	9,216	288	Advanced training, larger models
L40S	48GB	18,176	568	Large model training, high-performance ML

AWS AI Accelerators

Accelerator Type	vCPU	RAM	Accelerator Memory	Use Cases
TRAINIUM	8	32GB	32GB	High-performance training
INFERENTIA2	4	16GB	32GB	Next-gen inference optimization

Pre-installed GPU Software for CI/CD

Each runner comes with the following software pre-installed:

NVIDIA Device Drivers 555.58
CCUDA 12.1.0
cuDNN 9.2.1
NVIDIA Container Toolkit
AWS Neuron SDK

GitHub Actions Runner Specifications

Besides the GPU, Machine runners offer configurable CPU and RAM options to match your specific workload requirements.

Default Runner Specs and Credit Consumption

GPU Type	vCPUs	RAM	GPU/Accelerator RAM	Credits/Min (Spot)	Credits/Min (On-Demand)
T4G	4	8GB	16GB	1	3
T4	4	16GB	16GB	2	4
L4	4	16GB	24GB	2	6
A10G	4	16GB	24GB	3	7
L40S	4	32GB	48GB	3	14
TRAINIUM	8	32GB	32GB	-	-
INFERENTIA2	4	16GB	32GB	-	-

All runners include 100GB root volume storage.

Next Steps

Learn how to Configure Your Workflows for optimal performance
Explore Cost Optimization Strategies