{"id":77234,"date":"2026-04-02T00:59:41","date_gmt":"2026-04-01T21:59:41","guid":{"rendered":"https:\/\/saudishopper.com.sa\/en\/ollama-mlx-apple-silicon-preview\/"},"modified":"2026-05-12T17:43:00","modified_gmt":"2026-05-12T14:43:00","slug":"ollama-mlx-apple-silicon-preview","status":"publish","type":"post","link":"https:\/\/saudishopper.com.sa\/en\/ollama-mlx-apple-silicon-preview\/","title":{"rendered":"Ollama 0.19 Powered by MLX Brings Fastest AI Performance on Apple Silicon"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Ollama MLX Apple Silicon integration is now available in preview with the release of Ollama 0.19, marking a major leap in local AI performance on macOS. Powered by <a href=\"https:\/\/ml-explore.github.io\/mlx\/\" target=\"_blank\" rel=\"noopener\">Apple&#8217;s MLX machine learning framework<\/a>, this update takes full advantage of the unified memory architecture found in Apple Silicon chips to deliver dramatically faster inference speeds.<\/p>\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/files.ollama.com\/ollama_mlx.png\" alt=\"Ollama powered by MLX running at high speed on Apple Silicon\"\/><figcaption>Ollama 0.19 \u2014 built for speed on Apple Silicon with MLX<\/figcaption><\/figure>\n\n<h2 class=\"wp-block-heading\">What Is Ollama MLX Apple Silicon and Why It Matters<\/h2>\n\n<p class=\"wp-block-paragraph\">Until now, Ollama relied on a different backend for macOS inference. With version 0.19, the platform has been rebuilt on top of MLX, Apple&#8217;s own open-source machine learning framework designed specifically for Apple Silicon. The result is a significant performance boost across all Apple Silicon devices \u2014 from the M1 to the latest M5 series. For developers running <a href=\"\/category\/ai\/\">AI<\/a> workloads locally, this is a game-changing update.<\/p>\n\n<h2 class=\"wp-block-heading\">Benchmark Numbers: Prefill and Decode Speed<\/h2>\n\n<p class=\"wp-block-paragraph\">The performance gains are substantial. Testing conducted on March 29, 2026, using Alibaba&#8217;s Qwen3.5-35B-A3B model showed the following results on Apple&#8217;s M5 chip family:<\/p>\n\n<ul class=\"wp-block-list\"><li><strong>Prefill speed:<\/strong> 1,810 tokens\/s with Ollama 0.19 vs. 1,154 tokens\/s with Ollama 0.18 \u2014 a 57% improvement<\/li><li><strong>Decode speed:<\/strong> 112 tokens\/s with Ollama 0.19 vs. 58 tokens\/s with Ollama 0.18 \u2014 nearly double the output speed<\/li><li><strong>With int4 quantization:<\/strong> Ollama 0.19 reaches 1,851 tokens\/s prefill and 134 tokens\/s decode<\/li><\/ul>\n\n<p class=\"wp-block-paragraph\">On M5, M5 Pro, and M5 Max chips specifically, Ollama now leverages the new GPU Neural Accelerators to boost both time to first token (TTFT) and generation speed. This makes <a href=\"\/category\/computers\/\">Mac<\/a> machines genuinely competitive for serious local AI inference tasks.<\/p>\n\n<h2 class=\"wp-block-heading\">NVFP4 Support and Smarter Caching<\/h2>\n\n<p class=\"wp-block-paragraph\">Ollama 0.19 also introduces NVFP4 quantization support, a format developed by NVIDIA that maintains model accuracy while reducing memory bandwidth and storage requirements. This brings Ollama users closer to production-level inference parity \u2014 meaning the results you get locally will more closely match what cloud inference providers deliver at scale.<\/p>\n\n<p class=\"wp-block-paragraph\">Alongside NVFP4, the caching system has been overhauled with three key improvements relevant to <a href=\"\/category\/apps\/\">coding agents and AI applications<\/a>:<\/p>\n\n<ul class=\"wp-block-list\"><li><strong>Lower memory utilization:<\/strong> The cache is now reused across conversations, reducing memory overhead when using shared system prompts with tools like Claude Code<\/li><li><strong>Intelligent checkpoints:<\/strong> Snapshots are stored at smart locations in the prompt, reducing redundant processing and speeding up responses<\/li><li><strong>Smarter eviction:<\/strong> Shared prefixes survive longer even when older conversation branches are dropped<\/li><\/ul>\n\n<h2 class=\"wp-block-heading\">How to Get Started with Ollama 0.19<\/h2>\n\n<p class=\"wp-block-paragraph\">This preview release is optimized for the Qwen3.5-35B-A3B model with sampling parameters tuned for <a href=\"\/category\/education\/\">coding tasks<\/a>. Users need a Mac with more than 32GB of unified memory. Launch commands are straightforward:<\/p>\n\n<ul class=\"wp-block-list\"><li><strong>Claude Code:<\/strong> <code>ollama launch claude --model qwen3.5:35b-a3b-coding-nvfp4<\/code><\/li><li><strong>OpenClaw:<\/strong> <code>ollama launch openclaw --model qwen3.5:35b-a3b-coding-nvfp4<\/code><\/li><li><strong>Direct chat:<\/strong> <code>ollama run qwen3.5:35b-a3b-coding-nvfp4<\/code><\/li><\/ul>\n\n<p class=\"wp-block-paragraph\">The Ollama team has acknowledged contributions from the MLX contributor team, NVIDIA contributors to NVFP4 quantization and MLX CUDA support, the GGML and llama.cpp community, and the Alibaba Qwen team for open-sourcing their models. Future updates will expand supported model architectures and introduce easier import workflows for custom fine-tuned models.<\/p>","protected":false},"excerpt":{"rendered":"<p>Ollama 0.19 is now powered by Apple&#8217;s MLX framework, delivering up to 57% faster prefill speeds and nearly double the decode performance on Apple Silicon. The update also introduces NVFP4 quantization support and smarter caching for coding agents.<\/p>\n","protected":false},"author":16,"featured_media":77233,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_meta-robots-noindex":"","_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_pr_autopilot_social_copy":"{\"x_hook\":\"Ollama just dropped version 0.19 \u2014 now powered by Apple's MLX framework. On M5 chips, it hits 1,810 tokens per second prefill speed. That's nearly double what the previous version could do.\",\"x_takeaways\":\"Key Highlights:\\n\ud83d\udd39 Prefill speed jumps from 1,154 to 1,810 tokens\/s on Apple M5 chips\\n\ud83d\udd39 NVFP4 quantization brings production-level model accuracy to local inference\\n\ud83d\udd39 Smarter caching cuts memory use and speeds up coding agents like Claude Code\"}","_yoast_wpseo_opengraph_title":"Ollama 0.19 Powered by MLX Brings Fastest AI Performance on Apple Silicon","_yoast_wpseo_opengraph_description":"Ollama 0.19 leverages Apple's MLX framework to deliver record-breaking local AI inference speeds on Apple Silicon. With NVFP4 support and smarter caching, it's the most powerful version of Ollama yet for macOS developers.","_yoast_wpseo_opengraph_image":"https:\/\/saudishopper.com.sa\/en\/wp-content\/uploads\/2026\/04\/ollama-mlx-apple-silicon-preview.webp","_yoast_wpseo_opengraph_image_id":77233,"_yoast_wpseo_twitter_title":"Ollama 0.19 Is Now Powered by MLX on Apple Silicon","_yoast_wpseo_twitter_description":"Ollama 0.19 hits 1,810 tokens\/s prefill on M5 chips using Apple's MLX framework. NVFP4 support and smarter caching make it the fastest local AI setup on macOS yet.","_yoast_wpseo_twitter_image":"https:\/\/saudishopper.com.sa\/en\/wp-content\/uploads\/2026\/04\/ollama-mlx-apple-silicon-preview.webp","_yoast_wpseo_twitter_image_id":77233,"_yoast_wpseo_primary_category":449,"_extra_hide_author_box":"","_extra_featured_post":"","_partner_post_id":106512,"footnotes":""},"categories":[442,449,446],"tags":[2163,2466,1388,3023,2533],"class_list":["post-77234","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apps","category-ai","category-computers","tag-apple-silicon","tag-local-ai","tag-macos","tag-mlx","tag-ollama","et-has-post-format-content","et_post_format-et-post-format-standard"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ollama MLX Apple Silicon - Fastest Local AI | Saudi Shopper<\/title>\n<meta name=\"description\" content=\"Ollama MLX Apple Silicon preview is here with Ollama 0.19. 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