1. GPU-Accelerated VMs for Large Language Model Inference


    In order to build a cloud infrastructure that leverages GPU-accelerated virtual machines for large language model inference, we will need to provision GPU-equipped computing instances on a cloud provider that offers such capabilities.

    For the purpose of this exercise, let’s consider Google Cloud Platform (GCP), which provides Compute Engine virtual machines with GPU support. These instances can be tailored with machine types and GPUs that suit your inference workload requirements.

    To implement this on GCP using Pulumi, we would typically utilize the following resources:

    • gcp.compute.Instance: This resource is used to create and manage a VM instance in GCP Compute Engine. We can specify the machine type as well as attach GPU accelerators to our instance.
    • gcp.compute.AcceleratorType: This resource provides information about the types of GPUs available for use with Compute Engine instances.
    • Optionally, we might also use resources like gcp.compute.Disk for additional persistent storage if needed.

    We’ll write a Pulumi program in Python to create a GPU-accelerated Google Compute Engine instance:

    import pulumi import pulumi_gcp as gcp # Configuring a GPU-accelerated Compute Engine instance # For this example, we will be using an n1-standard-4 machine type and attaching a single NVIDIA Tesla K80 GPU. # Information on machine types and GPU options can be found on the GCP documentation: # https://www.pulumi.com/registry/packages/gcp/api-docs/compute/machinetype/ # https://www.pulumi.com/registry/packages/gcp/api-docs/compute/acceleratortype/ # Define the machine type machine_type = 'n1-standard-4' # Define the type and count of GPUs to attach gpu_accelerator_type = 'nvidia-tesla-k80' gpu_count = 1 # Create a new Google Compute Engine instance gpu_instance = gcp.compute.Instance('gpu-instance', machine_type=machine_type, zone='us-central1-a', # Replace with the desired zone boot_disk=gcp.compute.InstanceBootDiskArgs( initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs( image='debian-cloud/debian-9', # Optionally change the image ), ), # Attach the GPU to the instance guest_accelerators=[gcp.compute.InstanceGuestAcceleratorArgs( type=gpu_accelerator_type, count=gpu_count, )], network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs( network='default', access_configs=[gcp.compute.InstanceNetworkInterfaceAccessConfigArgs()], )], ) # Output the instance name and IP pulumi.export('instance_name', gpu_instance.name) pulumi.export('instance_ip', gpu_instance.network_interfaces.apply( lambda network_interfaces: network_interfaces[0].access_configs[0].nat_ip if network_interfaces[0].access_configs else None))

    This program defines a single GPU Compute Engine instance with the desired machine type and a specific number of GPUs. Here is a breakdown of the key components:

    • machine_type: The machine type for the instance. In this example, we're using n1-standard-4, which is suitable for a variety of general-purpose workloads. You can choose a machine type that fits your specific use case requirements.
    • gpu_accelerator_type and gpu_count: Specify the type of GPU and the number of GPUs to attach to the instance. Here we have chosen the nvidia-tesla-k80 accelerator and we’re attaching 1 GPU.
    • boot_disk: Specifies the boot disk for the instance. We're initializing the boot disk with the Debian 9 image, but you can change this to your preferred OS.
    • network_interfaces: Sets up the networking for the instance. In this example, we're using the default network with an automatically assigned public IP.

    Once you have written and applied this Pulumi program with the proper cloud provider configuration set up, Pulumi will provision the specified resources in your GCP account.

    Keep in mind that you will need to ensure your GCP account has the necessary quotas and permissions to create GPU-accelerated instances._GPU instances tend to incur higher costs as well, so it's essential to understand the billing implications before provisioning such resources.

    If you are using a different cloud provider, like AWS or Azure, you would use the corresponding resources offered by the Pulumi SDK for those providers with similar configurations adjusted for the respective cloud services (AWS EC2 instances with GPU, or Azure Virtual Machines with GPU).