Enterprises and developers increasingly run large language models on local hardware to retain data sovereignty, control costs, and avoid cloud vendor lock-in.
The Qwen vs Gemma comparison reveals that quantization resilience matters more than raw benchmarks. For local inference, quantization-aware training reshapes hardware, TCO, and data sovereignty: an analysis of structural implications.
A 92-billion-parameter mixture-of-experts model with a 512,000-token context window becomes CPU-runnable via integration into the ik_llama.cpp runtime. Techniques like MLA-latent cache and sparse activation lower the memory footprint, enabling on-pre...
A GPU-poor user opts for a quantized Gemma 4 12B as a personal assistant, proving that real-world utility often trumps size. The race for bigger LLMs hides a pragmatic truth: the winning model is the one that runs on your machine, with zero cloud cos...
After over a year of development, the first stable release of ExLlamaV3 lands with optimizations targeting the core bottlenecks of running LLMs on self-hosted hardware. The removal of flash-attn and xformers dependencies, new attention and convolutio...
A new 27-billion-parameter model embodies the tension between capability and sovereignty: compact enough to run locally, derived from Qwen, it promises to shake up enterprise deployment choices.
A community thank-you for a symbolic milestone in llama.cpp tells a deeper story: local inference on commodity hardware is now a production reality, reshaping deployment strategies, data sovereignty, and cost calculus for enterprises.
