Unify text, images, and audio in shared embedding spaces
Cross-modal embeddings represent a breakthrough in artificial intelligence, enabling understanding and reasoning across different data types within a unified representation space.
Deploy enterprise AI on budget hardware with open models
The democratization of AI is here. With open-source LLMs like Llama, Mistral, and Qwen now rivaling proprietary models, teams can build powerful AI infrastructure using consumer hardware - slashing costs while maintaining complete control over data privacy and deployment.
LongRAG, Self-RAG, GraphRAG - Next-gen techniques
Retrieval-Augmented Generation (RAG) has evolved far beyond simple vector similarity search. LongRAG, Self-RAG, and GraphRAG represent the cutting edge of these capabilities.
Speed-up FLUX.1-dev with GGUF quantization
FLUX.1-dev is a powerful text-to-image model that produces stunning results, but its 24GB+ memory requirement makes it challenging to run on many systems. GGUF quantization of FLUX.1-dev offers a solution, reducing memory usage by approximately 50% while maintaining excellent image quality.
Configure context sizes in Docker Model Runner with workarounds
Configuring context sizes in Docker Model Runner is more complex than it should be.
AI model for augmenting images with text instructions
Black Forest Labs has released FLUX.1-Kontext-dev, an advanced image-to-image AI model that augments existing images using text instructions.
Enable GPU acceleration for Docker Model Runner with NVIDIA CUDA support
Docker Model Runner is Docker’s official tool for running AI models locally, but enabling NVidia GPU acceleration in Docker Model Runner requires specific configuration.
Cut LLM costs by 80% with smart token optimization
Token optimization is the critical skill separating cost-effective LLM applications from budget-draining experiments.
GPT-OSS 120b benchmarks on three AI platforms
I dug up some interesting performance tests of GPT-OSS 120b running on Ollama across three different platforms: NVIDIA DGX Spark, Mac Studio, and RTX 4080. The GPT-OSS 120b model from the Ollama library weighs in at 65GB, which means it doesn’t fit into the 16GB VRAM of an RTX 4080 (or the newer RTX 5080).
Build MCP servers for AI assistants with Python examples
The Model Context Protocol (MCP) is revolutionizing how AI assistants interact with external data sources and tools. In this guide, we’ll explore how to build MCP servers in Python, with examples focused on web search and scraping capabilities.
Quick reference for Docker Model Runner commands
Docker Model Runner (DMR) is Docker’s official solution for running AI models locally, introduced in April 2025. This cheatsheet provides a quick reference for all essential commands, configurations, and best practices.
Compare Docker Model Runner and Ollama for local LLM
Running large language models (LLMs) locally has become increasingly popular for privacy, cost control, and offline capabilities. The landscape shifted significantly in April 2025 when Docker introduced Docker Model Runner (DMR), its official solution for AI model deployment.
ASICs and custom silicon push LLM inference speed and efficiency
The future of AI is not only about smarter models. It is also about silicon that matches how those models are actually served. Specialized hardware for LLM inference is following a path reminiscent of Bitcoin mining’s move from GPUs to purpose-built ASICs, only with harder constraints because models and precision recipes keep evolving.
Availability, real-world retail pricing across six countries, and comparison against Mac Studio.
NVIDIA DGX Spark is real, on sale Oct 15, 2025, and targeted at CUDA developers needing local LLM work with an integrated NVIDIA AI stack. US MSRP $3,999; UK/DE/JP retail is higher due to VAT and channel. AUD/KRW public sticker prices are not yet widely posted on 15/10/2025. The 29/10/2026 check is in the later street-price section.
Integrate Ollama with Go: SDK guide, examples, and production best practices.
This guide provides a comprehensive overview of available Go SDKs for Ollama and compares their feature sets.
Comparing Speed, parameters and performance of these two models
Here is a comparison between Qwen3:30b and GPT-OSS:20b focusing on instruction following and performance parameters, specs and speed.
