Deka GPU Documentations
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  • Deka LLM
    • Introduction
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    • Create a New LLM
    • Detail Deka LLM
      • Overview Tab
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    • API Deka LLM
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      • Embedding
    • Delete Deka LLM
    • How to Create Simple Prompt with Deka LLM
      • Create Deka LLM
      • Get URL API Deka LLM
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      • Access API Deka LLM using Postman
      • Get Model
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  • Deka Notebook
    • Introduction
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  • Reference
    • How to use kubeconfig on Linux
    • How to use kubeconfig on Windows
    • Kubernetes Commands for Enhancing Security
    • How to add GPU in Kubernetes
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      • Download kubeconfig
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      • Install Driver NVIDIA
    • RAPIDS
      • How to Setup RAPIDS
      • How to make Custom Image
    • How to push image with Docker
    • Deployment LLaMA 3.1 70B with VLLM on Kubernetes
      • Getting the Hugging Face API Key
      • Requesting Access to the LLaMA Model
      • Connect Kubernetes on Computer
      • Create Namespace
      • Create PersistentVolumeClaim (PVC)
      • Create Secret for Hugging Face Token
      • Create Deployment
      • Create Service
      • Verify Deployment
      • Accessing the LLaMA Service
      • Troubleshooting
    • How to Get an API Key on NGC
    • Deployment LLM with Deka GPU + NIM
    • Deployment Deepseek R1 70B with VLLM on Deka GPU's Kubernetes
      • Prerequisites
      • Create Namespace
      • Create PersistentVolumeClaim (PVC)
      • Create Deployment
      • Create Service
      • Verify Deployment
      • Accessing the Deepsek Service
      • Troubleshooting
    • How to Upload and Download on FTP Web
  • Troubleshooting
    • Reinstall Driver NVIDIA on Linux
    • NVIDIA Driver Not Detected After Upgrade Kernel
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On this page
  • Endpoint Deka LLM
  • 1. LLM
  • 2. VLM
  • 3. Embedding
  1. Deka LLM

Introduction

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Last updated 2 months ago

Deka LLM is one of the Deka GPU Product Services from Cloudeka that enables your business to utilize advanced AI models for generating and understanding text. This service simplifies access to high-level language models for tasks such as customer interactions, content creation, and analysis, without the need to manage the underlying infrastructure. If you already have access and have successfully logged into the Deka GPU Service Portal, select the LLM menu to navigate to the Deka LLM page.

Deka GPU Service Portal

To create Deka LLM in the Deka GPU Service Portal, you need to know the project type being used. You can learn more about the project type by checking .

Endpoint Deka LLM

Currently Deka LLM has the following model categories:

1. LLM

Very large machine learning models, trained on large amounts of text data to understand and generate natural language. LLM has functions for various NLP (Natural Language Processing) tasks such as language translation, question answering, text completion, and several other things.

2. VLM

Very large machine learning models, trained on large amounts of text data to understand and generate natural language. VLM has been used for tasks that require a combined understanding of images and text, such as describing an image, text-based image search, or answering questions based on images.

3. Embedding

Machine learning models that use numerical representations of objects, such as words, phrases, or images, in vector form in low-dimensional space. Embedding is used to capture the meaning or semantic features of the object. Embedding is used for models that require performing mathematical operations on non-numerical data, such as text or images.

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