{"id":2857,"date":"2025-09-13T08:40:51","date_gmt":"2025-09-13T08:40:51","guid":{"rendered":"https:\/\/www.markus-schall.de\/?p=2857"},"modified":"2026-05-02T05:48:17","modified_gmt":"2026-05-02T05:48:17","slug":"mlx-on-apple-silicon-as-local-ki-compared-with-ollama-co","status":"publish","type":"post","link":"https:\/\/www.markus-schall.de\/en\/2025\/09\/mlx-on-apple-silicon-as-local-ki-compared-with-ollama-co\/","title":{"rendered":"MLX on Apple Silicon as local AI in comparison with Ollama &amp; Co."},"content":{"rendered":"<p>At a time when centralized AI services such as ChatGPT, Claude or Gemini are dominating the headlines, many professional users are increasingly looking for an alternative - a local, self-controllable AI infrastructure. Especially for creative processes, sensitive data or recurring workflows, a local solution is often the more sustainable and secure option.<\/p>\n<p>Anyone working with a Mac - especially with Apple Silicon (M1, M2, M3 or M4) - can now find amazingly powerful tools to run their own language models directly on the device. At the center of this is a new, largely unknown component: MLX, a machine learning framework developed by Apple that is likely to play an increasingly central role in the company's AI ecosystem in the coming years.<!--more--><\/p>\n<hr \/>\n\n\t\t\t<div class=\"display-post-types\">\n\n\t\t\t\t\t\t\t<style type=\"text\/css\">\n\t\t\t#dpt-wrapper-948 { --dpt-text-align: left;--dpt-image-crop: center;--dpt-border-radius: 5px;--dpt-h-gutter: 10px;--dpt-v-gutter: 9px; }\t\t\t<\/style>\n\t\t\t<style type=\"text\/css\">#dpt-wrapper-948 { --dpt-title-font-style:normal;--dpt-title-font-weight:600;--dpt-title-line-height:1.5;--dpt-title-text-decoration:none;--dpt-title-text-transform:none;--dpt-excerpt-font-style:normal;--dpt-excerpt-font-weight:400;--dpt-excerpt-line-height:1.5;--dpt-excerpt-text-decoration:none;--dpt-excerpt-text-transform:none;--dpt-meta1-font-style:normal;--dpt-meta1-font-weight:400;--dpt-meta1-line-height:1.9;--dpt-meta1-text-decoration:none;--dpt-meta1-text-transform:none;--dpt-meta2-font-style:normal;--dpt-meta2-font-weight:400;--dpt-meta2-line-height:1.9;--dpt-meta2-text-decoration:none;--dpt-meta2-text-transform:none; }<\/style><div class=\"dpt-main-header\">\n\t\t\t\t\t\t<div class=\"dpt-main-title\">\n\t\t\t\t\t\t\t<span class=\"dpt-main-title-text\">Current articles on artificial intelligence<\/span>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t<\/div>\t\t\t\n\t\t\t\t<div id=\"dpt-wrapper-948\" class=\"dpt-wrapper dpt-mag1 land1 dpt-cropped dpt-flex-wrap\" >\n\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"wenn der mac zuh\u00f6rt: was apples integrierte ki mit gemini und siri k\u00fcnftig f\u00fcr nutzer bedeutet\" data-id=\"4813\"  data-category=\"apple iphone &amp; ipad apple macos ki-systeme\" data-post_tag=\"apple datenlogik datenschutz k\u00fcnstliche intelligenz llm mac prozesse sprachmodell\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/02\/if-the-mac-listens-to-what-apples-integrated-ki-with-gemini-and-siri-will-mean-for-users-in-the-future\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">When the Mac listens: What Apple's integrated AI with Gemini and Siri will mean for users in the future<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Apple, Siri and Gemini\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2026\/02\/if-the-mac-listens-to-what-apples-integrated-ki-with-gemini-and-siri-will-mean-for-users-in-the-future\/\" rel=\"bookmark\">When the Mac listens: What Apple's integrated AI with Gemini and Siri will mean for users in the future<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"ki als sparringspartner nutzen: wie denken im dialog produktiver wird\" data-id=\"4121\"  data-category=\"b\u00fccher ki-systeme tipps &amp; anleitungen\" data-post_tag=\"buch datenlogik denkmodelle k\u00fcnstliche intelligenz lernen llm ratgeber sprachmodell\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/12\/using-ki-as-a-sparring-partner-how-thinking-becomes-more-productive-in-dialog\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Using AI as a sparring partner: How thinking in dialog becomes more productive<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"AI as a savings partner\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/ki-als-sparringspartner-laptop.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/ki-als-sparringspartner-laptop.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ki-als-sparringspartner-laptop-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ki-als-sparringspartner-laptop-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ki-als-sparringspartner-laptop-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/12\/using-ki-as-a-sparring-partner-how-thinking-becomes-more-productive-in-dialog\/\" rel=\"bookmark\">Using AI as a sparring partner: How thinking in dialog becomes more productive<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"rag mit ollama und qdrant als universelle suchmaschine f\u00fcr eigene daten\" data-id=\"2764\"  data-category=\"filemaker &amp; erp ki-systeme\" data-post_tag=\"datenbanken docker k\u00fcnstliche intelligenz llama llm mistral ollama qdrant vektordatenbank\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/08\/rag-with-ollama-and-qdrant-as-a-universal-search-engine-for-your-own-data\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">RAG with Ollama and Qdrant as a universal search engine for own data<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1920\" height=\"640\" class=\"attachment-full size-full\" alt=\"Extend local AI with databases using RAG, Ollama and Qdrant\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant.jpg\" data-dpt-sizes=\"(max-width: 1920px) 100vw, 1920px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant.jpg 1920w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant-300x100.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant-1024x341.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant-768x256.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant-1536x512.jpg 1536w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lokale-ki-rag-ollama-qdrant-18x6.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/08\/rag-with-ollama-and-qdrant-as-a-universal-search-engine-for-your-own-data\/\" rel=\"bookmark\">RAG with Ollama and Qdrant as a universal search engine for own data<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"mit klarheit durch die krise: wie ki neue perspektiven er\u00f6ffnet\" data-id=\"2436\"  data-category=\"b\u00fccher\" data-post_tag=\"buch krisen k\u00fcnstliche intelligenz lernen pers\u00f6nlichkeitsentwicklung ratgeber\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/08\/with-clarity-through-the-crisis-how-ki-opens-up-new-perspectives\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Through the crisis with clarity: how AI opens up new perspectives<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"838\" height=\"1200\" class=\"attachment-full size-full\" alt=\"Book &#039;Crises as turning points - learn, grow, shape&#039;\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Krisen-Cover-Front-DEjpg.jpg\" data-dpt-sizes=\"(max-width: 838px) 100vw, 838px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Krisen-Cover-Front-DEjpg.jpg 838w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Krisen-Cover-Front-DEjpg-210x300.jpg 210w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Krisen-Cover-Front-DEjpg-715x1024.jpg 715w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Krisen-Cover-Front-DEjpg-768x1100.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Krisen-Cover-Front-DEjpg-8x12.jpg 8w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/08\/with-clarity-through-the-crisis-how-ki-opens-up-new-perspectives\/\" rel=\"bookmark\">Through the crisis with clarity: how AI opens up new perspectives<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\n<hr \/>\n<h2>Latest news on MLX and Ollama<\/h2>\n<p><strong>02.05.2026<\/strong>A current video goes into the topic in more depth and classifies the most important inference engines in detail. It becomes clear that it is not only the model itself that is decisive, but above all the \u201eengine\u201c behind it - i.e. the engine that controls the calculation, memory accesses and communication. The comparison between Ollama, MLX, llama.cpp and vLLM shows how performance and areas of application can differ greatly, depending on the hardware and objectives. The role of MLX on Apple-Silicon devices is particularly exciting, as new efficiency gains are possible here thanks to the architecture. At the same time, it is clear that there is no universal solution: Depending on the scenario - local, server-based or scaling - different engines make sense. The video therefore adds an important strategic perspective to the current MLX development.<\/p>\n<div class=\"lyte-wrapper\" style=\"width:640px;max-width:100%;margin:5px;\"><div class=\"lyMe\" id=\"WYL_e9pNIAH5kSs\"><div id=\"lyte_e9pNIAH5kSs\" data-src=\"https:\/\/www.markus-schall.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=%2F%2Fi.ytimg.com%2Fvi%2Fe9pNIAH5kSs%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\"><\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/e9pNIAH5kSs\" rel=\"nofollow noopener\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.markus-schall.de\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2Fe9pNIAH5kSs%2F0.jpg\" alt=\"YouTube video thumbnail\" width=\"640\" height=\"340\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:640px;margin:5px;\"><\/div><br \/>\nOllama, MLX, llama.cpp or vLLM? How to choose the motor for YOUR AI! | <a href=\"https:\/\/www.youtube.com\/@giorgi-ki\" target=\"_blank\" rel=\"nofollow noopener\">Giorgi Lomidze<\/a><\/p>\n<p><strong>30.03.2026<\/strong>: With the <a href=\"https:\/\/ollama.com\/blog\/mlx\" target=\"_blank\" rel=\"noopener\"><strong>current preview version<\/strong><\/a> Ollama integrates Apple's MLX framework as a backend for Apple-Silicon Macs for the first time. The aim is to significantly accelerate local AI and make better use of the hardware. MLX uses the unified memory architecture of modern Macs, allowing data to be shared efficiently between CPU and GPU without constant copying. The result is noticeable improvements in \u201etime to first token\u201c and generation speed.<\/p>\n<p>Initial benchmarks and reports speak of significant performance gains through to greatly increased token rates and more efficient memory usage. The video linked in the article clearly shows why this change is so important: the previous llama.cpp stack is being replaced by MLX, making local AI on the Mac truly fluid and suitable for everyday use for the first time. At the same time, the function remains a preview, meaning that limitations and further optimizations are to be expected. Overall, the move marks an important transition towards fast, locally running AI on consumer hardware.<\/p>\n<hr \/>\n<h2>What is MLX - and what does the new format stand for?<\/h2>\n<p>MLX is an open source framework from Apple for machine learning that is specially tailored to the hardware architecture of Apple Silicon. In contrast to other AI backends such as PyTorch or TensorFlow, MLX directly utilizes the advantages of Apple's so-called \u201eunified memory\u201c - i.e. the shared access of CPU and GPU to the same RAM area. This ensures significantly more efficient processing of data and models - especially for large language models, which can comprise several gigabytes.<\/p>\n<p>The associated MLX format typically describes models whose weights are stored in a compressed .npz file format (NumPy Zip). Models such as Mistral, Phi-2 or LLaMA 3 can be converted into this format using appropriate tools and run directly on a Mac - without a cloud, without an API, without restrictions.<\/p>\n<p>In another article I present a <a href=\"https:\/\/www.markus-schall.de\/en\/2025\/11\/apple-mlx-vs-nvidia-how-local-ki-inference-works-on-the-mac\/\"><strong>Comparison between Apple Silicon and NVIDIA<\/strong><\/a> and explain which hardware is suitable for running local language models on a Mac.<\/p>\n<h3>The current situation: What Apple already offers<\/h3>\n<p>With the announcement of <a href=\"https:\/\/en.wikipedia.org\/wiki\/Apple_Intelligence\" target=\"_blank\" rel=\"noopener\"><strong>Apple Intelligence<\/strong><\/a> in 2024, Apple has started to integrate system-wide AI functions directly into the operating system. Writing assistants, image processing, semantic search, intelligent mail functions - much of this runs completely locally, especially on devices with an M1 or newer chip. However, none of the new functions are available on older Intel Macs.<\/p>\n<p>At the same time, Apple has further developed the MLX framework and published it under an open license. In combination with tools such as mlx-lm or the new MLX Swift API, it is already possible to run text models locally, set up your own workflows or train models - directly on your own Mac, without data leaving the device.<\/p>\n<p>Professional users in particular - for example from the fields of software development, publishing, marketing or research - can benefit greatly from this, as MLX gives them completely new opportunities to integrate AI models into their workflows without having to rely on external providers.<\/p>\n<h2>How MLX works in practice<\/h2>\n<p>If you want to use MLX today, all you need is a terminal, Python (ideally in a separate virtual environment) and the mlx-lm package, which bundles all the necessary functions: Model download, quantization, inference and chat. After installation, ready-made models from the Hugging Face community can be started - for example:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">mlx_lm.chat --model mlx-community\/Mistral-7B-Instruct-v0.3-4bit<\/pre>\n<p>Alternatively, you can also access the API using a Python script. The models are automatically loaded, cached and executed locally - without an internet connection after the initial download.<\/p>\n<p>It is also easy to convert your own models. With a single command, you can download models from the Hugging Face Hub, quantize them and make them available for MLX:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">python -m mlx_lm.convert --hf-path meta-llama\/Llama-3-8B-Instruct -q<\/pre>\n<p>The resulting .npz files can then be permanently saved locally and reused.<\/p>\n<h3>The comparison: MLX vs. Ollama, Llama.cpp and LM Studio<\/h3>\n<p>In addition to MLX, there are several established alternatives for local AI use on the Mac - above all Ollama, Llama.cpp and LM Studio. Each of these tools has its strengths, but also specific limitations.<\/p>\n<h4>Ollama<\/h4>\n<p><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/08\/local-ki-on-the-mac-like-this-installo-create-a-language-model-with-ollama\/\"><strong>Ollama<\/strong><\/a> is particularly popular with developers because it offers a simple command line and REST API. The models are available here in the so-called GGUF format, an optimized file format for fast execution on local machines. Ollama is quick to set up, flexible and supports a wide range of models. However, Ollama does not currently run on the Mac with the MLX engine, but primarily uses a metal-based backend via llama.cpp.<\/p>\n<p>For workflows that require automation or headless operation (e.g. processes running in the background), Ollama is currently the first choice. However, if you want to use Apple's own optimizations, you will have to wait for future MLX integrations.<\/p>\n<h4>Llama.cpp<\/h4>\n<p>This project forms the basis for many other tools (including Ollama) and offers a very high-performance inference engine for GGUF models. It is extremely flexible, but not always easy to use or operate - especially for beginners. The big advantage: there is a huge community, many extensions and stable development.<\/p>\n<h4>LM Studio<\/h4>\n<p>Anyone looking for a graphical user interface usually ends up with LM Studio. The tool combines the download, administration and execution of language models in a lean, Mac-native app - including a chat interface, configuration and model management. The highlight: LM Studio has also supported the MLX engine for a few months now, allowing you to take full advantage of Apple's optimizations on an M1 or M2 Mac - and with significantly lower RAM consumption than comparable tools.<\/p>\n<p>LM Studio is the ideal entry point into the world of local AI, especially for users who don't want to bother with terminal commands - and in combination with MLX, it is a real high performer.<\/p>\n<h3>Fine-tuning made easy: How FileMaker 2025 brings LoRA training into everyday life<\/h3>\n<p><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/10\/lora-training-how-filemaker-2025-simplifies-the-fine-tuning-of-large-language-models\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-medium wp-image-3222\" src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/lora-finetuning-filemaker-300x200.jpg\" alt=\"LoRA Fine tuning - FileMaker 2025\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/lora-finetuning-filemaker-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lora-finetuning-filemaker-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lora-finetuning-filemaker-18x12.jpg 18w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/lora-finetuning-filemaker.jpg 1024w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>While MLX and Ollama show how language models can be operated locally and integrated into existing processes, the next step goes much further: the targeted adaptation of these models to your own data. This is precisely where the approach with <a href=\"https:\/\/www.markus-schall.de\/en\/2025\/10\/lora-training-how-filemaker-2025-simplifies-the-fine-tuning-of-large-language-models\/\"><strong>FileMaker 2025 from the Apple subsidiary Claris<\/strong><\/a> to. Instead of isolated training environments, a structured interface is created in which data records, training parameters and model versions can be managed centrally. LoRA training can be prepared, started and reproduced using clearly defined processes - without having to control each step manually via scripts or command lines. This turns experimental fine-tuning into a reproducible workflow that can be integrated into existing business processes. This is a decisive advantage, especially for companies that work with their own data: the AI does not remain general, but is trained specifically for its own context.<\/p>\n<h3>When MLX on Silicon is the better choice<\/h3>\n<p>While GGUF-based solutions (Ollama, Llama.cpp) are very flexible and run on many platforms, MLX scores with its deep integration into the Apple world. Particularly noteworthy are:<\/p>\n<ul>\n<li>Efficient memory utilization through unified memory<\/li>\n<li>Optimization for Metal\/GPU without complex configuration<\/li>\n<li>Seamless integration into Swift projects and Apple frameworks<\/li>\n<li>Future-proof, as Apple is actively developing the framework further<\/li>\n<li>Expandability, e.g. through own models, fine-tuning and system integration<\/li>\n<\/ul>\n<p>For Mac users who want to plan for the long term and retain full control over their data, MLX is already a promising entry into the world of local AI - with the potential to become the standard in the future.<\/p>\n<h3>From local AI to real business processes: Where ERP systems come into play<\/h3>\n<p><a href=\"https:\/\/www.markus-schall.de\/en\/erp-software\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-3182 size-medium\" src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/ERP-Software-300x200.jpg\" alt=\"ERP software\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/ERP-Software-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ERP-Software-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ERP-Software-18x12.jpg 18w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ERP-Software.jpg 1024w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>What is often underestimated in this context: The real strength of local AI systems such as MLX or Ollama lies not only in the model itself, but also in their integration into existing workflows. Interfaces and APIs in particular make it possible not to use AI in isolation, but to integrate it directly into operational processes - for example when analyzing data, automating texts or supporting decisions.<\/p>\n<p>This is precisely where a powerful ERP system becomes the central link: it provides the data, structures processes and ensures that AI results are not only generated but also processed in a meaningful way. Anyone who seriously wants to use local AI productively will therefore need a well thought-out system landscape in the long term. Further information can be found on the page <a href=\"https:\/\/www.markus-schall.de\/en\/erp-software\/\"><strong>ERP software<\/strong><\/a>, at which a FileMaker-based ERP system will be presented. FileMaker Server supports the hosting of MLX language models from version 2025 onwards <a href=\"https:\/\/www.markus-schall.de\/en\/2025\/09\/integration-of-mlx-in-filemaker-2025-local-ki-as-the-new-standard\/\">directly on the database server<\/a> and provides the corresponding script commands. The software from Apple subsidiary Claris runs on Apple Mac, Windows and mobile iOS devices.<\/p>\n<h3>An outlook: Where Apple wants to go with MLX and Apple Intelligence<\/h3>\n<p><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/02\/if-the-mac-listens-to-what-apples-integrated-ki-with-gemini-and-siri-will-mean-for-users-in-the-future\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-medium wp-image-4859\" src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-300x200.jpg\" alt=\"Apple, Siri and Gemini\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini-18x12.jpg 18w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-Siri-Gemini.jpg 1024w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>With WWDC 2025, Apple has clearly signaled that MLX is not a gimmick, but a strategic component in the growing Apple ecosystem around AI. The integration of the new \u201eFoundation Models\u201c directly into macOS and iOS, native Swift support and the further development of MLX in the direction of training, quantization and inference clearly show that Apple wants to get involved - but in its own way. In another article, I show where <a href=\"https:\/\/www.markus-schall.de\/en\/2026\/02\/if-the-mac-listens-to-what-apples-integrated-ki-with-gemini-and-siri-will-mean-for-users-in-the-future\/\"><strong>Apple with Siri and Gemini<\/strong><\/a> as part of the partnership with Google.<\/p>\n<p>In doing so, Apple remains true to its line: no spectacular promises, but solid, locally functioning technology that proves itself in the long term. For professional users, this is not only appealing, but also strategically highly interesting.<\/p>\n<p>MLX is well on the way to becoming the standard solution for local AI on the Mac. Those who are already working with it today are gaining a valuable head start - whether for creative, technical or analytical applications. In combination with tools such as mlx-lm, LM Studio or the new Swift API, a robust, reliable and future-proof AI environment can be created - in line with the controlled, data-sovereign way of working that will become increasingly important in the future.<\/p>\n<hr \/>\n<h3>Current survey on artificial intelligence<\/h3>\n<div class='bootstrap-yop yop-poll-mc'>\n\t\t\t\t\t\t\t<div class=\"basic-yop-poll-container\" style=\"background-color:#ffffff; border:0px; border-style:solid; border-color:#000000; border-radius:5px; padding:0px 5px;\" data-id=\"9\" data-temp=\"basic-pretty\" data-skin=\"square\" data-cscheme=\"blue\" data-cap=\"0\" data-access=\"guest\" data-tid=\"\" data-uid=\"e10f558045ebbf73609d5797489dc22d\" data-pid=\"2436\" data-resdet=\"votes-number,percentages\" data-show-results-to=\"guest\" data-show-results-moment=\"after-vote\" data-show-results-only=\"false\" data-show-message=\"true\" data-show-results-as=\"bar\" data-sort-results-by=\"as-defined\" data-sort-results-rule=\"asc\"data-is-ended=\"0\" data-percentages-decimals=\"2\" data-gdpr=\"no\" data-gdpr-sol=\"consent\" data-css=\".basic-yop-poll-container[data-uid] .basic-vote {\t\t\t\t\t\t\t\t\ttext-align: center;\t\t\t\t\t\t\t\t}\" data-counter=\"0\" data-load-with=\"1\" data-notification-section=\"top\"><div class=\"row\"><div class=\"col-md-12\"><div class=\"basic-inner\"><div class=\"basic-message hide\" style=\"border-left: 10px solid #008000; padding: 0px 10px;\" data-error=\"#ff0000\" data-success=\"#008000\"><p class=\"basic-message-text\" style=\"color:#000000; font-size:14px; font-weight:normal;\"><\/p><\/div><div class=\"basic-overlay hide\"><div class=\"basic-vote-options\"><\/div><div class=\"basic-preloader\"><div class=\"basic-windows8\"><div class=\"basic-wBall basic-wBall_1\"><div class=\"basic-wInnerBall\"><\/div><\/div><div class=\"basic-wBall basic-wBall_2\"><div class=\"basic-wInnerBall\"><\/div><\/div><div class=\"basic-wBall basic-wBall_3\"><div class=\"basic-wInnerBall\"><\/div><\/div><div class=\"basic-wBall basic-wBall_4\"><div class=\"basic-wInnerBall\"><\/div><\/div><div class=\"basic-wBall basic-wBall_5\"><div class=\"basic-wInnerBall\"><\/div><\/div><\/div><\/div><\/div><form class=\"basic-form\" action=\"\"><input type=\"hidden\" name=\"_token\" value=\"e01b83f2d7\" autocomplete=\"off\"><div class=\"basic-elements\"><div class=\"basic-element basic-question basic-question-text-vertical\" data-id=\"9\" data-uid=\"23706746c0f7d26a3c110a95d1734032\" data-type=\"question\" data-question-type=\"text\" data-required=\"yes\" data-allow-multiple=\"no\" data-min=\"1\" data-max=\"7\" data-display=\"vertical\" data-colnum=\"\" data-display-others=\"no\" data-others-color=\"\" data-others=\"\" data-others-max-chars=\"0\"><div class=\"basic-question-title\"><h5 style=\"color:#000000; font-size:16px; font-weight:normal; text-align:left;\">What do you think of locally running AI software such as MLX or Ollama?<\/h5><\/div><ul class=\"basic-answers\"><li class=\"basic-answer\" style=\"padding:0px 0px;\" data-id=\"45\" data-type=\"text\" data-vn=\"162\" data-color=\"#000000\" data-make-link=\"no\" data-link=\"\"><div class=\"basic-answer-content basic-text-vertical\"><label for=\"answer[45]\" class=\"basic-answer-label\"><input type=\"radio\" id=\"answer[45]\" name=\"answer[9]\" value=\"45\"><span class=\"basic-text\" style=\"color: #000000; font-size: 14px; font-weight: normal;\">Ingenious - finally independent of the cloud<\/span><\/label><\/div><\/li><li class=\"basic-answer\" style=\"padding:0px 0px;\" data-id=\"46\" data-type=\"text\" data-vn=\"32\" data-color=\"#000000\" data-make-link=\"no\" data-link=\"\"><div class=\"basic-answer-content basic-text-vertical\"><label for=\"answer[46]\" class=\"basic-answer-label\"><input type=\"radio\" id=\"answer[46]\" name=\"answer[9]\" value=\"46\"><span class=\"basic-text\" style=\"color: #000000; font-size: 14px; font-weight: normal;\">Interesting, but (still) too complicated<\/span><\/label><\/div><\/li><li class=\"basic-answer\" style=\"padding:0px 0px;\" data-id=\"47\" data-type=\"text\" data-vn=\"34\" data-color=\"#000000\" data-make-link=\"no\" data-link=\"\"><div class=\"basic-answer-content basic-text-vertical\"><label for=\"answer[47]\" class=\"basic-answer-label\"><input type=\"radio\" id=\"answer[47]\" name=\"answer[9]\" value=\"47\"><span class=\"basic-text\" style=\"color: #000000; font-size: 14px; font-weight: normal;\">I will try it out soon<\/span><\/label><\/div><\/li><li class=\"basic-answer\" style=\"padding:0px 0px;\" data-id=\"48\" data-type=\"text\" data-vn=\"5\" data-color=\"#000000\" data-make-link=\"no\" data-link=\"\"><div class=\"basic-answer-content basic-text-vertical\"><label for=\"answer[48]\" class=\"basic-answer-label\"><input type=\"radio\" id=\"answer[48]\" name=\"answer[9]\" value=\"48\"><span class=\"basic-text\" style=\"color: #000000; font-size: 14px; font-weight: normal;\">I don't need it - the cloud is enough for me<\/span><\/label><\/div><\/li><li class=\"basic-answer\" style=\"padding:0px 0px;\" data-id=\"49\" data-type=\"text\" data-vn=\"6\" data-color=\"#000000\" data-make-link=\"no\" data-link=\"\"><div class=\"basic-answer-content basic-text-vertical\"><label for=\"answer[49]\" class=\"basic-answer-label\"><input type=\"radio\" id=\"answer[49]\" name=\"answer[9]\" value=\"49\"><span class=\"basic-text\" style=\"color: #000000; font-size: 14px; font-weight: normal;\">I don't know exactly what that's about<\/span><\/label><\/div><\/li><\/ul><\/div><div class=\"clearfix\"><\/div><\/div><div class=\"basic-vote\"><a href=\"#\" class=\"button basic-vote-button\" role=\"button\" style=\"background:#027bb8; border:0px; border-style: solid; border-color:#000000; border-radius:5px; padding:10px 10px; color:#ffffff; font-size:14px; font-weight:normal;\">Vote<\/a><\/div><input type=\"hidden\" name=\"trp-form-language\" value=\"en\"\/><\/form><\/div><\/div><\/div><\/div>\n\t\t\t\t\t\t<\/div>\n<hr \/>\n<h2>Using MLX on the Mac - simple instructions for beginners<\/h2>\n<p>With MLX, Apple has created a new system that allows you to use artificial intelligence (AI) directly on your own Mac - without an internet connection, without the cloud, without dependence on Google or OpenAI. The great thing about it: if you have a Mac with an M1, M2, M3 or M4 processor (i.e. Apple Silicon), you can try MLX in just a few steps. Everything runs locally - your texts, questions and data never leave your computer.<\/p>\n<p>Here I explain step by step how to download and use a so-called language model with MLX. It sounds technical - but you'll see that it's easy to do.<\/p>\n<h3>Step 1: Check requirements<\/h3>\n<p>First you need:<\/p>\n<ul>\n<li>A Apple Silicon Mac (M1 or newer). You can find this in the system settings under \"About this Mac\".<\/li>\n<li>macOS 13 (Ventura) or newer.<\/li>\n<li>A working internet connection - only for downloading the model, after that everything runs offline.<\/li>\n<li>Some storage space, at least approx. 8-10 GB for a small model.<\/li>\n<\/ul>\n<p>You also need the program called \u201eTerminal\u201c, which is already pre1TP12 installed on every Mac. We use it to enter a few commands. You can find it on your Mac under \u201ePrograms\/Utilities\u201c or simply type  and then \u201eTerminal\u201c and confirm with \u201eEnter\u201c. Don't worry - you just need to copy and paste.<\/p>\n<h3>Step 2: Python 1TP12 animals (only if necessary)<\/h3>\n<p>MLX works with the Python programming language. Many Macs already have Python installiert. You can check whether it is available by entering the following in the terminal:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">python3 --version<\/pre>\n<p>If you get a version number (e.g. Python 3.10.6), you can continue directly.<\/p>\n<p>If not, I recommend using Homebrew to installieren (a popular tool for programs on the Mac). To do this, enter in the terminal:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">\/bin\/bash -c \"$(curl -fsSL https:\/\/raw.githubusercontent.com\/Homebrew\/install\/HEAD\/install.sh)\"<\/pre>\n<p>Then installier you Python with:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">brew install python<\/pre>\n<h3>Step 3: MLX-Tool 1TP12Animal<\/h3>\n<p>Now we download the MLX tool with which you can later use the language model. To do this, installier a small program called mlx-lm. Enter it in the terminal:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">pip3 install mlx-lm<\/pre>\n<p>This will take a few seconds. When it is finished, you are ready to load a model.<\/p>\n<p>Step 4: Download and start a model<\/p>\n<p>Now comes the exciting part: You get a real language model on your Mac - for example a version of Mistral, a very powerful, freely available AI model. Simply enter it into the terminal:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">mlx_lm.chat --model mlx-community\/Mistral-7B-Instruct-v0.3-4bit<\/pre>\n<p>This command does three things:<\/p>\n<ul>\n<li>The model is downloaded automatically (once).<\/li>\n<li>It is prepared and started.<\/li>\n<li>You end up in a chat window in the terminal where you can ask questions - similar to ChatGPT.<\/li>\n<\/ul>\n<p>When the download is complete (may take a few minutes depending on your internet speed), you will see a flashing cursor. You can now write, for example:<\/p>\n<p><em>Tell me something about the history of Venice.<\/em><\/p>\n<p>...and the model responds directly - completely offline.<\/p>\n<h3>Step 5: Continue working with the model<\/h3>\n<p>When you are finished, you can end the chat by typing exit or closing the window. Later you can reuse the same model without downloading it again by simply entering the same command again. The model is now stored locally on your Mac and remains there.<br \/>\nIf you would like to try out different models, you can do so via <strong><a href=\"https:\/\/huggingface.co\" target=\"_blank\" rel=\"noopener\">Hugging Face<\/a><\/strong> or change the model name directly in the terminal line - e.g:<\/p>\n<pre class=\"notranslate\" data-no-translation=\"\">mlx_lm.chat --model mlx-community\/Phi-2-4bit<\/pre>\n<p>Each model has a different style - some are factual, others are more creative or dialog-oriented.<\/p>\n<h2>Even easier? Use LM Studio as an interface<\/h2>\n<p>If you prefer to work with a mouse and window, you can also try the LM Studio program. It has a nice interface, supports MLX (on Apple Silicon) and allows you to download and use models with a click.<\/p>\n<p>You can get LM Studio here:<\/p>\n<p>\ud83d\udc49 <a href=\"https:\/\/lmstudio.ai\/\" target=\"_blank\" rel=\"noopener\"><strong>https:\/\/lmstudio.ai\/<\/strong><\/a><\/p>\n<p>After installation, you can select \u201eMLX\u201c as the engine in the settings - the program then uses the same technology as above, but in a pretty window with a chat field.<\/p>\n<p>You've done it - you can now use a modern AI completely locally on your Mac, without any cloud or subscription. Apple MLX makes it possible to operate language models efficiently, securely and in a privacy-friendly way.<\/p>\n<p>If you want to go even deeper later - for example, train your own models, improve them with your texts or incorporate them into your own software (such as FileMaker) - then MLX is the right way to go. But the first step is done: you have control back - and a powerful AI directly on your computer.<\/p>\n<h3>From local model to real AI memory<\/h3>\n<p><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/03\/chatgpt-data-export-explains-how-your-ki-chats-become-a-personal-knowledge-system\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-5296 size-medium\" src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/ChatGPT-Datenexport-300x200.jpg\" alt=\"ChatGPT data export\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/ChatGPT-Datenexport-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ChatGPT-Datenexport-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ChatGPT-Datenexport-18x12.jpg 18w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/ChatGPT-Datenexport.jpg 1024w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>The article on MLX clearly shows how powerful local AI has become directly on Apple hardware - especially thanks to the close integration with the architecture of Apple Silicon and optimized frameworks. Ollama focuses more on simple integration, APIs and flexible workflows, while MLX is deeply embedded in the Apple ecosystem and offers enormous long-term potential. This is precisely where the new series of articles comes in: It goes one step further and shows how these local AI models can be expanded to include a real \u201ememory\u201c. Instead of just executing models, a knowledge base is built up that <a href=\"https:\/\/www.markus-schall.de\/en\/2026\/03\/chatgpt-data-export-explains-how-your-ki-chats-become-a-personal-knowledge-system\/\"><strong>the data export of your own chat histories from ChatGPT<\/strong><\/a>\u00a0and makes it semantically searchable. This makes local AI not just a tool, but a personal system that grows with your own knowledge.<\/p>\n<hr \/>\n\n\t\t\t<div class=\"display-post-types\">\n\n\t\t\t\t\t\t\t<style type=\"text\/css\">\n\t\t\t#dpt-wrapper-949 { --dpt-text-align: left;--dpt-image-crop: center;--dpt-border-radius: 5px;--dpt-h-gutter: 10px;--dpt-v-gutter: 9px; }\t\t\t<\/style>\n\t\t\t<style type=\"text\/css\">#dpt-wrapper-949 { --dpt-title-font-style:normal;--dpt-title-font-weight:600;--dpt-title-line-height:1.5;--dpt-title-text-decoration:none;--dpt-title-text-transform:none;--dpt-excerpt-font-style:normal;--dpt-excerpt-font-weight:400;--dpt-excerpt-line-height:1.5;--dpt-excerpt-text-decoration:none;--dpt-excerpt-text-transform:none;--dpt-meta1-font-style:normal;--dpt-meta1-font-weight:400;--dpt-meta1-line-height:1.9;--dpt-meta1-text-decoration:none;--dpt-meta1-text-transform:none;--dpt-meta2-font-style:normal;--dpt-meta2-font-weight:400;--dpt-meta2-line-height:1.9;--dpt-meta2-text-decoration:none;--dpt-meta2-text-transform:none; }<\/style><div class=\"dpt-main-header\">\n\t\t\t\t\t\t<div class=\"dpt-main-title\">\n\t\t\t\t\t\t\t<span class=\"dpt-main-title-text\">Social issues of the present<\/span>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t<\/div>\t\t\t\n\t\t\t\t<div id=\"dpt-wrapper-949\" class=\"dpt-wrapper dpt-mag1 land1 dpt-cropped dpt-flex-wrap\" >\n\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"nord stream sprengung: sabotage, machtpolitik und die unbequemen offenen fragen\" data-id=\"4441\"  data-category=\"allgemein gesellschaft\" data-post_tag=\"deutschland energiepolitik europa geopolitik krisen meinungsfreiheit sicherheitspolitik spieltheorie\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/01\/north-stream-blasting-sabotage-power-politics-and-the-uncomfortable-open-questions\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Nord Stream demolition: sabotage, power politics and the uncomfortable unanswered questions<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Nord Stream blasting\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/nordstream-sprengung.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/nordstream-sprengung.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/nordstream-sprengung-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/nordstream-sprengung-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/nordstream-sprengung-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2026\/01\/north-stream-blasting-sabotage-power-politics-and-the-uncomfortable-open-questions\/\" rel=\"bookmark\">Nord Stream demolition: sabotage, power politics and the uncomfortable unanswered questions<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"die stille gefahr von wearables: wenn bequemlichkeit zur \u00dcberwachung wird\" data-id=\"3560\"  data-category=\"allgemein apple iphone &amp; ipad gesellschaft gesundheit hardware\" data-post_tag=\"apple datenschutz gesundheit ratgeber\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/11\/the-silent-danger-of-wearables-when-convenience-becomes-surveillance\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">The silent danger of wearables: when convenience becomes surveillance<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Wearables, smartwatch, in-ear headphones\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/wearables-smartwatch-daten.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/wearables-smartwatch-daten.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/wearables-smartwatch-daten-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/wearables-smartwatch-daten-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/wearables-smartwatch-daten-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/11\/the-silent-danger-of-wearables-when-convenience-becomes-surveillance\/\" rel=\"bookmark\">The silent danger of wearables: when convenience becomes surveillance<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"digitales eigentum erkl\u00e4rt &#8211; so entstehen nachhaltige online-verm\u00f6genswerte\" data-id=\"4766\"  data-category=\"allgemein b\u00fccher featured gesellschaft ki-systeme\" data-post_tag=\"buch datenbanken denkmodelle digitales eigentum erp-software k\u00fcnstliche intelligenz prozesse publishing\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/02\/digital-property-explained-how-sustainable-online-assets-are-created\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Digital ownership explained - How sustainable online assets are created<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"What is digital property\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Digitales-Eigentum.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Digitales-Eigentum.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Digitales-Eigentum-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Digitales-Eigentum-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Digitales-Eigentum-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2026\/02\/digital-property-explained-how-sustainable-online-assets-are-created\/\" rel=\"bookmark\">Digital ownership explained - How sustainable online assets are created<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"beruf, weltbild, zukunft: entscheidungen im schatten des umbruchs\" data-id=\"3197\"  data-category=\"allgemein gesellschaft kunst &amp; kultur tipps &amp; anleitungen\" data-post_tag=\"erfahrungen krisen pers\u00f6nlichkeitsentwicklung ratgeber\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/10\/career-world-view-future-decisions-in-the-shadow-of-upheaval\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Career, world view, future: Decisions in the shadow of upheaval<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Decisions in the shadow of upheaval\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Entscheidungen-im-Schatten-Umbruch.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Entscheidungen-im-Schatten-Umbruch.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Entscheidungen-im-Schatten-Umbruch-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Entscheidungen-im-Schatten-Umbruch-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Entscheidungen-im-Schatten-Umbruch-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/10\/career-world-view-future-decisions-in-the-shadow-of-upheaval\/\" rel=\"bookmark\">Career, world view, future: Decisions in the shadow of upheaval<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\n<hr \/>\n<h2>Frequently asked questions<\/h2>\n<ol>\n<li><strong>What exactly is MLX - and how does it differ from PyTorch or TensorFlow?<\/strong><br \/>\nMLX is a machine learning framework developed by Apple and specifically optimized for Apple Silicon (M1-M4). Unlike PyTorch or TensorFlow, which target many platforms, MLX specifically uses the architecture of Apple chips - e.g. the common memory structure (unified memory) and metal GPU acceleration. This makes it more memory efficient and faster on Macs - but only on Apple hardware.<\/li>\n<li><strong>Why should you choose MLX over a tool like Ollama or Llama.cpp?<\/strong><br \/>\nMLX has an advantage if you are working specifically on Apple Silicon and want to get the maximum performance out of the device. Ollama and Llama.cpp are very flexible, but often run less efficiently on the Mac. MLX can also be integrated directly into Swift projects - ideal for developers building applications close to Apple. It is not a competitor to Ollama - but a specialized tool for professionals.<\/li>\n<li><strong>Which models are compatible with MLX?<\/strong><br \/>\nMany open language models are compatible - such as Mistral, LLaMA 2 and 3, Phi-2 or TinyLLaMA - which are either already converted or can be converted using the mlx-lm.convert tool. It is important that they are available in NumPy-ZIP format (.npz) and are prepared for MLX. There is now a separate section on Hugging Face for MLX-compatible models.<\/li>\n<li><strong>How easy is it to get started? Do I have to be a developer?<\/strong><br \/>\nA little technical understanding is helpful - e.g. for the terminal, Python environments or model names. But getting started is relatively easy thanks to mlx-lm: one installation command, one command to start, done. If you prefer to work with a user interface, you can use LM Studio - it now also supports MLX on the Mac.<\/li>\n<li><strong>Can I also train MLX for my own projects - e.g. with my own texts?<\/strong><br \/>\nYes, you can - but the training is currently intended more for advanced users. Most users use MLX models for inference (i.e. for answering, text generation, etc.). For training or fine-tuning, you need to be familiar with LoRA, data formats (JSONL) and memory requirements - or use tools such as FileMaker 2025, which simplify this process.<\/li>\n<li><strong>What about security and data protection at MLX?<\/strong><br \/>\nVery good - because MLX runs completely locally. All data, inputs and model responses remain on your own computer. There is no cloud transfer, no external API - ideal for data-sensitive projects, internal documents, protected customer data or confidential notes.<\/li>\n<li><strong>What role does Apple itself play in this? Will MLX be developed further?<\/strong><br \/>\nApple has published MLX under an open license and is actively developing it further - especially in connection with Apple Intelligence, the AI system for macOS, iOS and iPadOS. At WWDC 2025, MLX was presented as the official framework for integrating custom language models into Apple software. It can be assumed that MLX will continue to gain importance in the Apple world.<\/li>\n<li><strong>Can I also combine MLX with other tools, e.g. Neo4j, n8n or FileMaker?<\/strong><br \/>\nYes - MLX is a pure ML framework, but it can be connected to other tools via REST APIs, custom Python services or local wrappers. For example, you can integrate it into your own automation (n8n), a semantic database (Neo4j) or FileMaker solutions - the latter is now even available natively with FileMaker 2025<\/li>\n<\/ol>\n<p>Image (c) Monoar_CGI_Artist @ pixabay<\/p>\n<hr \/>\n\n\t\t\t<div class=\"display-post-types\">\n\n\t\t\t\t\t\t\t<style type=\"text\/css\">\n\t\t\t#dpt-wrapper-950 { --dpt-text-align: left;--dpt-image-crop: center;--dpt-border-radius: 5px;--dpt-h-gutter: 10px;--dpt-v-gutter: 9px; }\t\t\t<\/style>\n\t\t\t<style type=\"text\/css\">#dpt-wrapper-950 { --dpt-title-font-style:normal;--dpt-title-font-weight:600;--dpt-title-line-height:1.5;--dpt-title-text-decoration:none;--dpt-title-text-transform:none;--dpt-excerpt-font-style:normal;--dpt-excerpt-font-weight:400;--dpt-excerpt-line-height:1.5;--dpt-excerpt-text-decoration:none;--dpt-excerpt-text-transform:none;--dpt-meta1-font-style:normal;--dpt-meta1-font-weight:400;--dpt-meta1-line-height:1.9;--dpt-meta1-text-decoration:none;--dpt-meta1-text-transform:none;--dpt-meta2-font-style:normal;--dpt-meta2-font-weight:400;--dpt-meta2-line-height:1.9;--dpt-meta2-text-decoration:none;--dpt-meta2-text-transform:none; }<\/style><div class=\"dpt-main-header\">\n\t\t\t\t\t\t<div class=\"dpt-main-title\">\n\t\t\t\t\t\t\t<span class=\"dpt-main-title-text\">Current articles on art &amp; culture<\/span>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t<\/div>\t\t\t\n\t\t\t\t<div id=\"dpt-wrapper-950\" class=\"dpt-wrapper dpt-mag1 land1 dpt-cropped dpt-flex-wrap\" >\n\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"reichweite ist kein eigentum &#8211; warum sichtbarkeit heute nicht mehr ausreicht\" data-id=\"3994\"  data-category=\"allgemein filemaker &amp; erp gesellschaft kunst &amp; kultur\" data-post_tag=\"datenlogik datenschutz denkmodelle digitales eigentum erp-software\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/12\/reach-is-not-ownership-why-visibility-is-no-longer-enough-today\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Reach is not ownership - Why visibility is no longer enough today<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Reach vs. ownership\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/reichweite-vs-eigentum.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/reichweite-vs-eigentum.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/reichweite-vs-eigentum-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/reichweite-vs-eigentum-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/reichweite-vs-eigentum-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/12\/reach-is-not-ownership-why-visibility-is-no-longer-enough-today\/\" rel=\"bookmark\">Reach is not ownership - Why visibility is no longer enough today<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"mehr als punk: nina hagen, cosma shiva und die kunst, sich nicht vereinnahmen zu lassen\" data-id=\"4521\"  data-category=\"allgemein gesellschaft kunst &amp; kultur\" data-post_tag=\"auswandern deutschland erfahrungen europa krisen meinungsfreiheit musik portrait\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/01\/more-than-punk-nina-hagen-cosma-shiva-and-the-art-of-not-being-taken-in\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">More than punk: Nina Hagen, Cosma Shiva and the art of not letting yourself be taken in<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Portrait of Nina and Cosma Shiva Hagen\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Nina-Cosma-Shiva-Hagen-Titel.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Nina-Cosma-Shiva-Hagen-Titel.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Nina-Cosma-Shiva-Hagen-Titel-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Nina-Cosma-Shiva-Hagen-Titel-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Nina-Cosma-Shiva-Hagen-Titel-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2026\/01\/more-than-punk-nina-hagen-cosma-shiva-and-the-art-of-not-being-taken-in\/\" rel=\"bookmark\">More than punk: Nina Hagen, Cosma Shiva and the art of not letting yourself be taken in<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"apple im wandel: fr\u00fche ger\u00e4te, eigene erfahrungen und eine ausstellung im ocm\" data-id=\"5480\"  data-category=\"allgemein apple iphone &amp; ipad apple macos hardware kunst &amp; kultur stories &amp; humor\" data-post_tag=\"apple erfahrungen filemaker mac prozesse publishing\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2026\/03\/apple-in-transition-early-devices-own-experiences-and-an-exhibition-at-ocm\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Apple in transition: Early devices, personal experiences and an exhibition at the OCM<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1024\" height=\"683\" class=\"attachment-full size-full\" alt=\"Apple Macintosh Classic and Color Classic\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-OCM-Titel.jpg\" data-dpt-sizes=\"(max-width: 1024px) 100vw, 1024px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-OCM-Titel.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-OCM-Titel-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-OCM-Titel-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/Apple-OCM-Titel-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2026\/03\/apple-in-transition-early-devices-own-experiences-and-an-exhibition-at-ocm\/\" rel=\"bookmark\">Apple in transition: Early devices, personal experiences and an exhibition at the OCM<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"dpt-entry has-thumbnail\" data-title=\"dieter bohlen im klartext: warum deutschland an der eigenen b\u00fcrokratie scheitert\" data-id=\"3546\"  data-category=\"allgemein b\u00fccher gesellschaft kunst &amp; kultur\" data-post_tag=\"buch deutschland erfahrungen krisen meinungsfreiheit musik\">\n\t\t\t\t\t\t\t<div class=\"dpt-entry-wrapper\"><div class=\"dpt-featured-content\"><div class=\"dpt-permalink\"><a href=\"https:\/\/www.markus-schall.de\/en\/2025\/11\/dieter-bohlen-in-plain-language-why-germany-is-failing-because-of-its-own-bureaucracy\/\" class=\"dpt-permalink\"><span class=\"screen-reader-text\">Dieter Bohlen in plain language: Why Germany is failing because of its own bureaucracy<\/span><\/a><\/div><div class=\"dpt-thumbnail\"><div class=\"dpt-thumbnail-inner\"><img width=\"1536\" height=\"1024\" class=\"attachment-full size-full\" alt=\"Dieter Bohlen in conversation with Dominik Kettner\" context=\"dpt\" data-dpt-src=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/bohlen-kettner-germany.jpg\" data-dpt-sizes=\"(max-width: 1536px) 100vw, 1536px\" data-dpt-srcset=\"https:\/\/www.markus-schall.de\/wp-content\/uploads\/bohlen-kettner-germany.jpg 1536w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/bohlen-kettner-germany-300x200.jpg 300w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/bohlen-kettner-germany-1024x683.jpg 1024w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/bohlen-kettner-germany-768x512.jpg 768w, https:\/\/www.markus-schall.de\/wp-content\/uploads\/bohlen-kettner-germany-18x12.jpg 18w\" \/><\/div><span class=\"dpt-thumbnail-aspect-ratio\" style=\"padding-top: 75%\"><\/span><\/div><\/div><div class=\"sub-entry\"><h3 class=\"dpt-title\"><a class=\"dpt-title-link\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/11\/dieter-bohlen-in-plain-language-why-germany-is-failing-because-of-its-own-bureaucracy\/\" rel=\"bookmark\">Dieter Bohlen in plain language: Why Germany is failing because of its own bureaucracy<\/a><\/h3><\/div><\/div>\n\t\t\t\t\t\t<\/div><!-- .dpt-entry -->\n\t\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\n<hr \/>","protected":false},"excerpt":{"rendered":"<p>In einer Zeit, in der zentrale KI-Dienste wie ChatGPT, Claude oder Gemini die Schlagzeilen beherrschen, w\u00e4chst bei vielen professionellen Anwendern das Bed\u00fcrfnis nach einem Gegenpol &#8211; einer lokalen, selbst kontrollierbaren KI-Infrastruktur. Gerade f\u00fcr kreative Prozesse, sensible Daten oder wiederkehrende Arbeitsabl\u00e4ufe ist eine lokale L\u00f6sung oft die nachhaltigere und sicherere Option. Wer mit einem Mac arbeitet &#8230; <a title=\"Dieter Bohlen in plain language: Why Germany is failing because of its own bureaucracy\" class=\"read-more\" href=\"https:\/\/www.markus-schall.de\/en\/2025\/11\/dieter-bohlen-in-plain-language-why-germany-is-failing-because-of-its-own-bureaucracy\/\" aria-label=\"Read more about Dieter Bohlen in plain language: Why Germany is failing because of its own bureaucracy\">Read more<\/a><\/p>","protected":false},"author":1,"featured_media":2865,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":9788,"footnotes":""},"categories":[15,431],"tags":[469,471,435,433,437,465,432,434],"class_list":["post-2857","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apple-macos","category-ki-systeme","tag-datenschutz","tag-kuenstliche-intelligenz","tag-llama","tag-llm","tag-mistral","tag-mlx","tag-ollama","tag-sprachmodell"],"_links":{"self":[{"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/posts\/2857","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/comments?post=2857"}],"version-history":[{"count":24,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/posts\/2857\/revisions"}],"predecessor-version":[{"id":5975,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/posts\/2857\/revisions\/5975"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/media\/2865"}],"wp:attachment":[{"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/media?parent=2857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/categories?post=2857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.markus-schall.de\/en\/wp-json\/wp\/v2\/tags?post=2857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}