A growing number of enterprises and developers are moving to self-hosted AI models to retain data control, reduce cloud costs, and work around regulatory restrictions on cloud LLMs.
Surging demand for AI accelerators is congesting air and sea freight, driving up shipping rates. For enterprises building on-premise LLM deployments, the logistics squeeze complicates TCO calculations and spells potential delays in server and cluster...
DeepSeek releases the V4-Pro-DSpark model on Hugging Face along with the DSpark technical paper. This release fuels the strategy of those betting on self-hosted LLMs and data sovereignty, reducing cloud dependency.
The race for on-prem Large Language Models often collides with a concrete constraint: the availability of professional GPUs. Operating without data-center hardware, by leveraging consumer cards, CPUs, and aggressive quantization, demands a deep under...
A new survey reframes continual learning as an ecosystem problem, not just an algorithmic one. For those running models in production, five design principles emerge, tackling plasticity loss, capability inheritance, and operational sustainability.
No new KVM virtualization features for ARM64 are in the Linux 7.2 merge window. The absence is blamed on an extraordinary workload of AI-related fixes consuming the ARM Linux development team's bandwidth, while AMD, Intel, s390, and RISC-V all see im...
OpenAI has launched an initiative to find and patch vulnerabilities in open-source projects. This matters for organizations running LLMs locally, as key serving components like vLLM, llama.cpp, and Ollama could now see security attention that was pre...
From the 1955 proposal to the LLM explosion, AI has cycled through winters and springs. Today, the spread of generative models brings data control and technological sovereignty to the fore, pushing many organizations to consider self-hosted deploymen...
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...
The recent Trump administration move against Anthropic, discussed on the Equity podcast, is more than politics. For enterprises evaluating where to run their LLMs, it signals a real risk: dependence on cloud providers can become a strategic bottlenec...
