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I will deploy your llm on kubernetes with vllm, docker and GPU support
AI Voice and Automation Engineer, DevOps
Hi 👋 I'm Khuzaima Ahmed. I’m passionate about helping small businesses unlock the power of AI Automations to save time, cut costs, and stay competitive.
I help businesses implement AI Agents that c...
Level 1
Has met certain performance criteria and shows strong potential in the marketplace.
Highly Responsive
Known for exceptionally quick replies
About this Gig
Have an open-source LLM model working locally but need it as a production-ready API?
I will help you deploy your Hugging Face or open-source LLM on Kubernetes using a production-ready AI deployment stack: vLLM, Docker, CUDA, Kubernetes Deployment, GPU nodes, Service, Ingress, and OpenAI-compatible API endpoints.
What I can help you with:
- Review your model, use case, and hardware requirements
- Select a compatible Hugging Face or custom model
- Configure the right inference engine: vLLM, TensorRT-LLM, or SGLang
- Package the model, libraries, and runtime into a deployable container
- Prepare Kubernetes manifests for your AI service
- Configure GPU-aware deployment for compatible clusters
- Set up Service or Ingress for API access
- Create an OpenAI-compatible endpoint where supported
- Test the full request and response flow
- Provide clear handover documentation
Supported model families may include Qwen, Llama, Mistral, Gemma, DeepSeek, and other compatible Hugging Face models.
Please message me before ordering so I can review your model, cloud provider, Kubernetes setup, GPU availability, and deployment requirements.
Tools:
Kubernetes
•
Docker
Frameworks:
Terraform
•
Pulumi
•
Ansible
•
Crossplane
•
Other
Programming language:
Bash
•
Go
•
JavaScript
•
Python
•
Other
Expertise:
Installation
•
Debugging
•
Configuration
My Portfolio
FAQ
Why should I message you before ordering?
LLM deployment depends heavily on model size, GPU memory, Kubernetes setup, and API requirements. Messaging first helps confirm compatibility, avoid delays, and choose the right package.
Can you help choose the right model for my hardware?
Yes. I can review your GPU, VRAM, use case, and performance goals to suggest a suitable model size and deployment approach before starting the project.
What access do you need to complete the deployment?
I may need access to your Kubernetes cluster, cloud console, container registry, model repository, domain or ingress details, and deployment environment. Exact access depends on your setup.
Can you deploy a fine-tuned or custom model?
Yes, if the model is compatible with the selected inference engine and your available hardware. Please share the model details before ordering so I can confirm feasibility.
Are cloud, GPU, or server costs included?
No. Cloud, GPU, domain, storage, and infrastructure costs are not included in the gig price. You are responsible for providing the required server, cluster, and cloud resources.
Do I need to already have a Kubernetes cluster?
Yes, you should have access to a Kubernetes cluster or cloud environment. If you do not have one yet, message me first and I can guide you on what setup is required before deployment.
Do you provide GPU setup and configuration?
I can configure GPU-aware Kubernetes deployment for compatible clusters. This may include GPU node selection, resource configuration, CUDA-compatible runtime setup, and basic validation.
Which LLM models can you deploy?
I can help deploy compatible Hugging Face or open-source models such as Qwen, Llama, Mistral, Gemma, DeepSeek, and similar models. Final compatibility depends on your GPU, VRAM, model size, and deployment setup.
Do you support vLLM, TensorRT-LLM, and SGLang?
Yes. vLLM is the default option for most deployments. Depending on your model, GPU, and performance needs, I can also work with TensorRT-LLM or SGLang for more advanced inference setups.
Will you provide an OpenAI-compatible API endpoint?
Yes, where supported by the inference engine and model setup, I can expose an OpenAI-compatible API endpoint so your app can call the private LLM similar to standard chat completion APIs.
