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As developers, we're rapidly integrating Large Language Models (LLMs) into AI agents, building powerful applications that can plan, execute tools, and generate complex responses. While a single-user prototype might run
Fellow developers, the quest for faster, more cost-effective LLM inference just took a significant leap forward. DeepSeek, known for its open-source contributions, has unveiled DSpark, a new framework designed to
This article explores how a 10-year-old Intel Xeon E5-2620 v4 server with 128 GB DDR3 RAM and no GPU can run a modern LLM like Gemma 4 26B-A4B at reading speed. It highlights that LLM inference is often memory-bound and showcases deep optimization techniques using `ik_llama.cpp`, including speculative decoding, CPU-aware MoE routing, advanced memory management, and specialized attention kernels. The success demonstrates that granular software control can unlock significant performance on older, abundant-RAM hardware.
