The local execution of large language models using tools like llama.cpp, Ollama, and quantized models is booming, with models running on consumer GPUs, phones, and browsers. This shift reinforces data sovereignty and cost control, reshaping deployment strategies.
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...
PrismML released Bonsai 27B, a dense LLM using 1-bit quantization to shrink from 54 GB to 3.8 GB and run locally in the browser via custom WebGPU kernels, retaining 90% of intelligence. A major step for on-device AI and data sovereignty.
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.
Two mining-era NVIDIA P102-100 cards, costing around $100 combined, run Qwen 35B via llama.cpp, serving three concurrent users at 23 tokens per second. A showcase of how on-premise inference becomes affordable thanks to llama.cpp’s CUDA 12.x compatib...
The $65M round backed by Benchmark marks a coming of age for the open source tool that lets developers run AI models on their own PCs. The milestone reflects a structural shift: local inference is no longer a hobby but a real bet on sovereignty, cont...
