Technical insights and best practices for AI & Machine Learning
Learn how to maximize your AI inference performance with GPU slicing technology on CoolVDS.
Your expensive GPUs are idling because your system memory can't keep up. We dissect the specific kernel parameters, NUMA topologies, and PyTorch configurations required to saturate DDR5 bandwidth on modern Linux servers.
Stop running fragile AI agents on your laptop. A battle-hardened guide to deploying resilient, stateful agent swarms using Docker, Pgvector, and NVMe-backed infrastructure in Norway.
Deploying text, image, and audio models in a single pipeline is a resource nightmare. We dissect the architecture of a real-time multi-modal API, covering ONNX optimization, AVX-512 CPU inference, and why data sovereignty in Norway matters for AI workloads in 2025.
Compliance, latency, and cost are driving Nordic CTOs toward self-hosted LLMs. Learn how to deploy quantized Mistral models on high-performance infrastructure in Oslo.
Stop blaming OpenAI for your latency. Learn how to optimize Vector DB storage, async Python middleware, and caching layers on high-performance NVMe VPS architecture in Norway.
Deploying Generative AI in Norway requires more than just an API key. Learn how to architect a secure, high-performance RAG layer on CoolVDS to leverage Claude while keeping your proprietary data safe on Norwegian soil.
Move beyond basic API calls. Learn how to architect robust Google Gemini integrations using Python, Redis caching, and secure infrastructure on Linux, tailored for Norwegian data compliance standards.
NVIDIA hardware is expensive and scarce. This guide details how to deploy AMD ROCm 6.1 for high-performance ML workloads, covering kernel configuration, Docker passthrough, and the critical NVMe I/O requirements often ignored by cloud providers.
Stop bleeding cash on external API tokens. Learn how to deploy production-grade AI inference using NVIDIA NIM containers on high-performance Linux infrastructure. We cover the Docker setup, optimization flags, and why data sovereignty in Oslo matters.
Move beyond fragile shell scripts. Learn to architect robust Kubeflow Pipelines (KFP) for reproducible ML workflows, ensuring GDPR compliance and minimizing latency in Norwegian infrastructure.
Escape the Python GIL and scale ML workloads across nodes without the Kubernetes overhead. A technical guide to deploying Ray on high-performance NVMe VPS in Norway for GDPR-compliant AI computing.
Stop wasting GPU memory on fragmentation. Learn how to deploy vLLM with PagedAttention for 24x higher throughput, keep your data compliant with Norwegian GDPR, and optimize your inference stack on CoolVDS.
Move your LLM applications from fragile local scripts to robust production environments. We analyze the specific infrastructure requirements for LangChain, focusing on reducing RAG latency, handling PII scrubbing under GDPR, and optimizing Nginx for Server-Sent Events.
Retrieval-Augmented Generation (RAG) is the architecture of 2023, but outsourcing your vector database poses massive compliance risks. Learn how to deploy a high-performance, self-hosted vector engine using pgvector on NVMe infrastructure in Oslo.
With NVIDIA H100 shortages squeezing European startups, smart CTOs are looking at AMD's Instinct roadmap. Here is a technical deep-dive on running PyTorch on ROCm, KVM GPU passthrough, and why Norway is the best place to host power-hungry AI workloads in 2023.
The H100 Hopper architecture changes the economics of LLM training, but raw compute is worthless without IOPS to feed it. We dissect the H100's FP8 capabilities, PyTorch 2.0 integration, and why Norway's power grid is the secret weapon for AI ROI.
Stop relying on throttled public APIs. A battle-tested guide to deploying a production-ready Stable Diffusion 1.5 instance with Automatic1111, xformers, and secure Nginx reverse proxies on high-performance Norwegian infrastructure.
It is January 2023, and conversational AI is booming. But sending Norwegian customer data to US APIs is a compliance minefield. Here is how to build a low-latency, privacy-preserving AI proxy layer.
Forget the cloud API trap. Learn how to deploy GDPR-compliant BERT pipelines on high-performance local infrastructure using PyTorch and efficient CPU inference strategies.
OpenAI's GPT-3 API is changing the industry, but GDPR and Schrems II make it a legal minefield for Nordic businesses. We explore self-hosting viable alternatives like DistilBERT and GPT-2 on high-performance NVMe VPS infrastructure.
Cloud latency kills real-time AI. In the wake of the Schrems II ruling, moving inference to the edge isn't just about performance—it's about compliance. Here is the 2020 architecture for deploying quantized TensorFlow models on Norwegian infrastructure.
Stop wrapping Flask around your models. Learn how to deploy PyTorch 1.5 with TorchServe, optimize for CPU inference on NVMe VPS, and navigate the data sovereignty minefield just created by the ECJ.
Stop wrapping your Keras models in Flask. Learn how to deploy TensorFlow Serving via Docker on high-performance NVMe infrastructure for sub-100ms inference times while keeping your data compliant with Norwegian standards.
Stop burning budget on V100s for simple inference. We benchmark the new NVIDIA T4 against the Pascal generation and show you how to deploy mixed-precision models on Ubuntu 18.04 using nvidia-docker2.
Stop letting Python's GIL kill your production latency. We explore how to bridge PyTorch 1.0 and production environments using the new ONNX Runtime, ensuring sub-millisecond responses on dedicated Norwegian infrastructure.
Latency kills AI projects. We dissect CPU threading, TensorFlow 1.x configurations, and why NVMe storage is non-negotiable for production models in 2019.
It is 2017, and TensorFlow 1.0 has changed the game. But throwing a Titan X at your model is useless if your I/O is choking the pipeline. Here is how to architecture a training stack that actually saturates the bus, strictly for Norwegian data compliance.
Stop serving models with Flask. Learn how to deploy TensorFlow 1.0 candidates using gRPC and Docker for sub-millisecond inference latency on Norwegian infrastructure.
In 2017, the rush to Machine Learning is overwhelming, but your infrastructure choices might be sabotaging your results. We dissect why NVMe storage and KVM isolation are non-negotiable for data science workloads in Norway.