Background MTP support was recently merged into llama.cpp through the following pull request: MTP support merged into llama.cpp After the merge, I wanted to test MTP models on my mini-PC powered by the AMD AI MAX 395. I tried several approaches, including manually building llama.cpp and using Unsloth GGUF models directly. However, despite multiple attempts, I could not get a stable working setup. I also searched through GitHub issues and asked several AI assistants, including ChatGPT, Gemini, and DeepSeek.

Introduction Last year, I spent some time studying Oracle Banking Microservices Architecture (OBMA), together with enterprise schedulers and orchestration platforms such as Control-M . Part of the work involved understanding how to convert traditional Control-M jobs into Airflow DAGs. During this process, I started to observe an important architectural distinction: Not all workflows are the same. While studying OBMA, I noticed that Netflix Conductor was used as the workflow engine inside the architecture.

Introduction When working with AMD GPUs and ROCm-based AI workloads, one common issue appears when using uv for Python dependency management. The problem becomes especially visible when setting up projects like ComfyUI on Linux with ROCm-enabled PyTorch builds. Although ROCm-specific wheels are manually installed, running commands such as uv add or dependency synchronization may silently replace ROCm-enabled packages with standard PyPI versions that only support CUDA. This leads to broken GPU acceleration and unexpected runtime failures.