You will get your custom LLM deployed on RunPod using Docker and vLLM
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Top Rated
Project details
With extensive experience in full-stack development and AI deployment, I specialize in building and deploying custom language models (LLMs) that are optimized for performance and scalability. In this project, I will dockerize your custom LLM and deploy it on RunPod using vLLM, ensuring that it runs efficiently in a containerized environment. This approach not only ensures portability but also enhances performance by taking full advantage of hardware acceleration and parallelism.
Your custom LLM will be built to handle large-scale workloads and high concurrency with minimal overhead. By utilizing Docker, the deployment process becomes more streamlined, reproducible, and scalable, making it easier to manage in both development and production environments.
You can expect:
• A fully containerized solution that will be easy to deploy and manage on RunPod.
• Optimized performance with vLLM to accelerate inference speeds.
• A robust, secure deployment ready for production use.
My approach emphasizes strong communication throughout the project to ensure your needs are met, and the deployment process is smooth.
Your custom LLM will be built to handle large-scale workloads and high concurrency with minimal overhead. By utilizing Docker, the deployment process becomes more streamlined, reproducible, and scalable, making it easier to manage in both development and production environments.
You can expect:
• A fully containerized solution that will be easy to deploy and manage on RunPod.
• Optimized performance with vLLM to accelerate inference speeds.
• A robust, secure deployment ready for production use.
My approach emphasizes strong communication throughout the project to ensure your needs are met, and the deployment process is smooth.
AI Development Type
Model Tuning, Recommendation SystemAI Tools
Amazon SageMaker, deeplearn.js, Keras, NVIDIA AI Platform, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$400
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100
Add Gradio/Streamlit UI for quick demo
(+ 1 Day)
+$75
Integrate LangChain for chatbot/agent use
(+ 2 Days)
+$200
Embedding model deployment (e.g., BGE/Instruct)
(+ 1 Day)
+$100Frequently asked questions
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Harasis is the greatest specialist I met and a good person as well. I highly recommend him for your projects.
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About Harasis
Full Stack Developer| AI Automation & Custom SaaS (Python, React, API)
100%
Job Success
Bakersfield, United States - 1:56 pm local time
I specialize in developing CRM systems, workflow automation, voice/chat bots, and full-stack applications using Python (FastAPI), React/Next.js, and modern APIs. My focus is on solving real business problems through scalable and reliable solutions.
Recent work includes:
✅ Full-stack marketplace and SaaS platforms with complex integrations
✅ Custom CRM system for lead management and workflow automation
✅ Voice-based automation system using Twilio for handling customer interactions
I write clean, maintainable code, communicate clearly, and deliver solutions that are built to scale.
If you’re looking to automate processes, build a SaaS product, or integrate AI into your system, I’m here to help.
Steps for completing your project
After purchasing the project, send requirements so Harasis can start the project.
Delivery time starts when Harasis receives requirements from you.
Harasis works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Setup and Assessment
Review the provided materials, such as the GitHub repository and documentation, to understand the scope and current status of the project.
Dockerization and Environment Setup
Create a Dockerfile to containerize the application, ensuring it can be run in isolated environments. Set up necessary Docker Compose configurations if required.