A growing number of enterprises and developers are turning to locally run AI models to ensure data sovereignty, predictable costs, and independence from cloud APIs.
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.
PrismML quantized the Qwen3.6-27B model down to 1 bit, shrinking it from 54GB to 3.9GB. Bonsai 27B runs on an iPhone 15 Pro Max with 8GB RAM, retaining ~90% benchmark performance. Math holds up, but knowledge and reasoning slip. A decisive step for l...
OpenLLM-France releases Luciole-23B-Instruct-1.1, a multilingual causal model under Apache 2.0 license, also available in 8B and 1B sizes. Trained on the Jean Zay supercomputer in three stages, it covers math, code, RAG, and translation. The real sig...
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 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.
