Ditching Ollama for Llama.cpp: DeepSeek-R1 32B Performance Fix on Linux & AMD Instinct

⚡️ From Ollama to Llama.cpp: DeepSeek-R1 32B Model on AMD Instinct Mi60
In this advanced tutorial, we are migrating our powerful local LLM setup from Ollama to the bare-metal efficiency of Llama.cpp on a Linux system, utilizing the formidable AMD Instinct Mi60 32GB HBM2 GPU.

If you followed my previous guide on setting up the DeepSeek-R1 32B Model with a custom Python UI and Ollama (link below), you know the power of local AI. However, we encountered annoying issues like server stalls and zombie processes that required constant manual intervention.

The focus of this tutorial is simple: Llama.cpp is faster, more stable, and offers granular control over your hardware, completely eliminating the server issues we faced with Ollama. We'll show you the dramatic performance and stability gains on the AMD Instinct Mi60, making your DeepSeek-R1 32B (and other large models) a reliable, high-speed powerhouse.

Topics Covered:

Why Llama.cpp provides superior speed and control for local LLMs.

Resolving "zombie" process and server stopping issues common with Ollama.

Setting up Llama.cpp with ROCm support on Linux for the AMD Mi60.

Migrating the DeepSeek-R1 32B GGUF model for optimal performance.

🔗 Resources & Links
Previous Ollama & DeepSeek-R1 Tutorial: https://www.ojambo.com/web-ui-for-ai-deepseek-r1-32b-model

My Programming Books on Amazon: https://www.amazon.com/stores/Edward-Ojambo/author/B0D94QM76N

My Programming Courses: https://ojamboshop.com/product-category/course

One-on-One Programming Tutorials (Contact): https://ojambo.com/contact

AI Installation & Migration Services (Contact): I can install AI solutions like Gemma-The-Writer-J.GutenBerg-10B for chat or Wan 3.3 TI2V 5B for video generation, or migrate your existing solutions. https://ojamboservices.com/contact

#LlamaCpp #Ollama #AMDInstinct #Mi60 #LocalLLM #DeepSeekR1 #LinuxAI #HBM2 #AIGPU #EdwardOjambo