Local-First AI Infrastructure Engineer
Marcus Thorne is an AI infrastructure engineer focused on optimizing large language models and multimodal AI for on-device deployment without cloud dependencies. With an MSc in machine learning and 7+ years architecting production inference pipelines, Marcus specializes in quantization techniques, ONNX runtime optimization, and efficient model serving on commodity hardware. His expertise spans Llama, Gemma, and other open models, with deep knowledge of techniques like 4-bit quantization, low-rank adaptation (LoRA), and flash attention. Marcus has optimized inference performance across CPU, GPU, and NPU targets, making privacy-first AI accessible on edge devices. At Vucense, Marcus writes about practical on-device AI deployment, inference optimization, and building truly private AI applications that never send data to external servers.
Dirty Frag CVE-2026-43284 and CVE-2026-43500: Linux kernel LPE exploiting memory fragmentation. Active exploitation, affected distributions, mitigation patches, and defense strategies
Mythos and Cyber are advanced vulnerability-detection AI security models. Anthropic and OpenAI restrict access claiming responsible AI, but gatekeeping…
Fine-tune large language models locally with QLoRA and Unsloth on Ubuntu 24.04 in 2026. Covers dataset preparation, LoRA configuration, training on RTX 4090, evaluation, GGUF export, and Ollama deployment.
Build a fully local RAG pipeline in Python 2026. Ollama embeddings, pgvector 0.8 HNSW search, and Llama 4 Scout for document Q&A. No OpenAI. No cloud. Zero data leaves your machine.
Complete llama.cpp tutorial for 2026. Install, compile with CUDA/Metal, run GGUF models, tune all inference flags, use the API server, speculative decoding, and benchmark your hardware.
Install Ollama 5.x on Ubuntu, macOS, and Windows. Pull and run Llama 4, Qwen3, Gemma 3, and Mistral locally. REST API setup, GPU acceleration, Open WebUI, and sovereign model management.
Speculative decoding doubles local LLM inference speed with zero quality loss. How it works, how to enable it in Ollama and llama.cpp today, and which model pairs give the best speedup.
Master GGUF quantization formats for local LLMs in 2026. Q2_K, Q4_K_M, Q5_K_S, Q8_0, F16 explained with benchmarks, VRAM tables, and exact Ollama and llama.cpp commands.
William Shatner's battle with AI-generated 'death' rumors has exposed a dark side of platform risk.
Sony has raised PlayStation 5 prices again, citing a global helium shortage and regional conflict.
With 52 million monthly downloads and 135,000 local models on HuggingFace, Ollama and local AI inference have officially moved from niche hobby to…
Gartner predicts 80% of governments will deploy AI agents by 2028. We analyse NVIDIA's Agent Toolkit, NemoClaw, and what autonomous governance means for.
Meta and AMD's $60B AI chip deal and 6-gigawatt GPU rollout signal a major shift in compute sovereignty.
A major analysis argues US AI governance is breaking down. We contrast governance risk with capacity building and explain why AI sovereignty is the.
How the Pentagon's Maven Smart System and Claude are creating an AI targeting engine that doubles strike tempos in the 2026 US–Iran conflict — and the…
Get a free NVIDIA API key, access GLM-4.7, and build a working AI app in Python. No cloud lock-in. Full sovereign dev setup with Streamlit.
Ditch expensive subscriptions. The 12 best free, open-source creative tools in 2026 — ranked for sovereignty, privacy, and professional-grade output.
