What version of ROCm is supported?

The optimization parameters and environment variables in this skill are designed for ROCm version 6.0 and higher.

Is this skill compatible with NVIDIA GPUs?

While the hardware context is specifically tuned for AMD RX 7900 XTX and ROCm, the API references, Modelfile structures, and routing logic are compatible with any Ollama installation.

How does this skill handle multiple models running at once?

It implements a priority-based loading strategy that categorizes models by use case (e.g., primary, secondary, embeddings) and manages VRAM allocation to prevent out-of-memory errors.

Can I use this for Home Assistant integration?

Yes, the skill includes specialized Modelfiles and Python integration patterns designed to translate natural language into specific Home Assistant service calls.

Ollama Model Management & Optimization

Name: Ollama Model Management & Optimization
Author: Lobbi-Docs

byLobbi-Docs

Data Science & ML

Optimizes local LLM orchestration and GPU performance for Ollama-integrated AI environments.

About

The Ollama Mastery skill provides a comprehensive framework for managing local large language models within the Ahling Command Center. It specializes in optimizing performance for AMD ROCm hardware, specifically the RX 7900 XTX, and provides sophisticated patterns for multi-model routing, VRAM management, and custom Modelfile creation. Whether you are building RAG pipelines, smart home controllers, or multi-agent workflows with AutoGen, this skill ensures your local AI infrastructure is efficient, responsive, and properly allocated.

Key Features

Custom Modelfile templates for Home Assistant and Coordinator agents
Automated VRAM allocation and concurrent model loading strategies
0 GitHub stars
Dynamic multi-model routing based on task complexity
Integration patterns for RAG pipelines and Microsoft AutoGen
AMD ROCm GPU optimization for RX 7900 XTX hardware

Use Cases

Building privacy-focused RAG pipelines with local embeddings
Orchestrating specialized models for complex smart home automation
Deploying a high-performance local AI server on AMD hardware

About

Key Features

Custom Modelfile templates for Home Assistant and Coordinator agents
Automated VRAM allocation and concurrent model loading strategies
0 GitHub stars
Dynamic multi-model routing based on task complexity
Integration patterns for RAG pipelines and Microsoft AutoGen
AMD ROCm GPU optimization for RX 7900 XTX hardware

Use Cases

Building privacy-focused RAG pipelines with local embeddings
Orchestrating specialized models for complex smart home automation
Deploying a high-performance local AI server on AMD hardware