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Createdat

ID of the custom template used for the resource, if applicable.

Customtemplateid

Number of GPUs allocated to the resource.

Gpucount

Name

Price per hour for using the resource (average).

Pricehr

Type of socket used by the GPU, if applicable.

ID of the team to which the resource is assigned, if applicable.

Teamid

Timestamp when the resource was terminated.

Terminatedat

Type of the pod, either EXTERNAL or HOSTED.

Userid

APIPodHistoryConfig

EnvironmentTypeEnum

Details

Param

ErrorDetail

GPUSocket

GPUType

PodTypeEnum

ProviderTypeEnum

HTTPBearer

GenericPaginationResponse[List[APIPodHistoryConfig]]

ErrorResponse

Introduction

The most cost effective GPUs deployed in less than 1min

Quickstart - Deploy a Pod

Run Jupyter Notebooks

Deploy your private API instance of Llama3.1 via the VLLM inference library.

Deploy Llama via VLLM

Deploy Custom Docker Image

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Deploy Multi-Node Cluster

How to use Torch FSDP to train models distributed across multiple nodes.

Run Torch FSDP

Run Megatron-Deepspeed to train models distributed across multiple nodes.

Run Megatron-Deepspeed

Run Huggingface Accelerate to train models distributed across multiple nodes.

Run Hugging Face Accelerate

Run Llama 405B inference in BF16 across multiple nodes.

Llama 405B Inference in BF16

Distill Llama 405B into smaller models via our Multi-node offering.

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Deploy compute on Prime Intellect and contribute to the decentralized training of INTELLECT-1.

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