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 How to Set Up a Local LLM Inference Server Using Ollama. Running a Large Language Model (LLM) on your own server is the ultimate way to keep your data private. It keeps your prompts on infrastructure you control and replaces unpredictable per-token API fees with a flat monthly cost. Ollama is one of the simplest tools to get this done: it bundles model downloads, GPU acceleration, and a local OpenAI-compatible HTTP API into a single setup. Here is what you need to know before deploying it in production. 1. Hardware and VRAM Sizing For full-speed AI inference, your model must fit entirely in your GPU memory (VRAM). If a model is too large and spills over into system RAM, Ollama will split the workload with the CPU, which drastically slows down generation speeds. For mid-size to large models, deploying on bare-metal gpu servers (like an NVIDIA A40 or A100) ensures you have the dedicated memory headroom required. 2. Verifying GPU Acceleration After installing your NVIDIA drivers ...

 How to Migrate a WordPress Site Without Downtime When your website outgrows shared hosting, moving to a dedicated server is the best way to secure steadier performance and full control over your environment. However, the migration process itself is where things often go wrong. A missed database change or a poorly timed DNS switch can mean broken pages or lost WooCommerce orders. The Zero-Downtime Strategy A guaranteed zero-downtime move requires a specific sequence of events. You cannot simply copy your files and change your DNS immediately. 1. Lower Your DNS TTL At least 24-48 hours before the move, reduce your DNS Time-to-Live (TTL) to 300 seconds. This ensures that when you finally switch servers, the global internet updates quickly. 2. The Hosts File Trick Before you change your public DNS, you must test the new server. By editing your computer's local /etc/hosts file, you can force your own browser to load the new server while the rest of the world still sees the old one....

 AI Training vs Inference: NVIDIA H100 or A40? Not every AI workload needs the most powerful GPU on the market. Renting more hardware than your project actually requires is one of the most common and expensive mistakes tech teams make when scaling AI infrastructure. When choosing between the NVIDIA H100 and NVIDIA A40 , the decision comes down to one core question: Are you doing AI Training or AI Inference? The Training Heavyweight: NVIDIA H100 (Hopper) If you are training large language models (LLMs) from scratch, fine-tuning multi-billion parameter models, or running HPC-grade research, the NVIDIA H100 is purpose-built for the job. Transformer Engine: Uses dynamic FP8/FP16 precision switching to accelerate the complex matrix operations that transformer models rely on. HBM3 Memory: Delivers ultra-high memory bandwidth, preventing data bottlenecks during large-batch, distributed training. NVLink Support: Allows high-speed GPU-to-GPU interconnectivity across multi-node clusters....