A growing number of enterprises and developers are moving AI inference to local hardware, driven by cost, privacy, and control. Tools like Ollama and llama.cpp have seen massive adoption, with billions of parameters now running on consumer GPUs and even phones.
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 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...
After buying a 32GB VRAM GPU and running a 5-bit quantized 31B model, a Reddit user finds it blows away the standard free ChatGPT model. The episode exposes a potential downgrading of OpenAI's free tier and strengthens the case for self-hosting when ...
