Unlocking Generative AI Potential: A Comprehensive Guide to Integrating Foundation Models with Amazon Bedrock - Amazon Web Services
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1hUlBuVy14amFUR20zYmZ0VDRxQjh0UVFZclRyZklsaGVPOGdFdmREcFl6cHVaNThTQkQ4SDVEUDBaTF8ydHFDbmJGVEpmWEd2MkctbWhib2lDMzRzbVJr?oc=5" target="_blank">Unlocking Generative AI Potential: A Comprehensive Guide to Integrating Foundation Models with Amazon Bedrock</a> <font color="#6f6f6f">Amazon Web Services</font>
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Vulkan backend much easier on the CPU and GPU memory than CUDA.
On linux and compiled my own llama.cpp with CUDA support, top would always show one pegged CPU core at 100% when running Qwen3.5-9B-GGUF:Q4_K_M on my potato like RTX A2000 12GB. Also, nvidia-smi would show 11GB+ of GPU memory usage. Speed is ~30 tokens per second. My system fans would spin up when this single core gets pegged which was annoying to listen to. Decided to compile llama.cpp again with Vulkan backend to see if anything would be different. Well it was a big difference when using the exact same model Now, top is only showing one CPU core at about 30% usage and nvidia-smi is only showing 7.2GB of GPU memory usage. Speed is the same at ~30 tokens per second. No longer have my system fan spinning up when running inferencing. Just curious why the GPU memory footprint is lower and CPU
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