A growing shift towards running AI models locally on proprietary hardware, driven by data sovereignty, cost control, and latency needs. Companies and developers are embracing self-hosting, edge inference, and on-premise LLM deployments.
Memory management in Linux 7.2 brings a 30-100% throughput boost for MongoDB, thanks to the MGLRU algorithm. The improvement matters for data-heavy workloads and infrastructure, with potential downstream benefits for on-premise deployments relying on...
The Spanish company argues that running inference on endpoints is the way to contain the soaring costs of cloud-based AI. A stance that reignites the debate over where models should be run.
Anthropic’s latest position paper outlines a frontier AI vision. Yet for many practitioners, the immediate response was a retreat to local models. We dig into the drivers – data sovereignty, cost control, latency – and analyze the trade-offs between ...
A server with two Radeon AI PRO R9700 GPUs and 64 GB total VRAM runs Qwen 3.6 27B at Q8 quantization with Multi-Token Prediction. Decode reaches 67 tok/s on full contexts, prefill exceeds 1,500 t/s, and prompt caching works efficiently—a concrete loo...
A Reddit megathread sparks debate on AI agents running locally with open-weight models. Amid shaky definitions and ‘Harness’ hype, real-world choices hinge on autonomy, hardware control, and software maturity. For on-premise deployments, the discussi...
