{"id":39385,"date":"2026-08-12T09:44:00","date_gmt":"2026-08-12T09:44:00","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=39385"},"modified":"2026-08-12T13:44:35","modified_gmt":"2026-08-12T13:44:35","slug":"google-runs-gemma-ai-offline-on-raspberry-pi-5-with-litert","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/google-runs-gemma-ai-offline-on-raspberry-pi-5-with-litert\/","title":{"rendered":"Google Runs Gemma AI Offline on Raspberry Pi 5 With LiteRT"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">Google AI Edge has published a detailed guide showing how to deploy its Gemma 4 language model entirely offline on a Raspberry Pi 5 using LiteRT, its on-device inference runtime. The setup costs roughly $80 in hardware, requires no cloud connection, and hits generation speeds twice as fast as normal human speech.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-b0ffac9c wp-block-group-is-layout-flex\"><div style=\"font-size:16px\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Google AI Edge published a developer guide this week demonstrating something that would have seemed impractical just a year ago: running a capable large language model fully offline on a Raspberry Pi 5 \u2014 the $80 single-board computer that students and hobbyists have used for robotics and tinkering projects for over a decade. The toolchain involves two Google-built components, LiteRT and Gemma 4, and the bar to get started is now a single <code>pip install<\/code>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Google Actually Announced<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The announcement centers on LiteRT, Google&#8217;s on-device inference runtime (evolved from TensorFlow Lite), and a specialized orchestration layer on top of it called LiteRT-LM, which handles LLM deployment specifically. Together, they let developers run Gemma 4 E2B \u2014 one of the smallest models in Google&#8217;s open Gemma family \u2014 directly on a Pi 5&#8217;s quad-core ARM Cortex-A76 processor, no internet connection required.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The performance numbers are more impressive than the hardware might suggest. On a Raspberry Pi 5, LiteRT-LM achieves 99 tokens per second for prefill and 9 tokens per second for decode while running Gemma 4 E2B, with a peak memory footprint of just 1,432 MB. Because Gemma 4 E2B&#8217;s tokenizer packs roughly 4.2 characters into each token, that decode rate translates to an end-to-end generation speed of approximately 27.3 characters per second \u2014 about 300 words per minute, or twice the pace of normal human speech.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To show what that means in practice, Google demoed a Reachy Mini robot powered entirely by a Raspberry Pi 5. The robot can see, hear and respond to its environment in real time using a split processing architecture: LLM inference runs on the CPU, while vision and audio models are offloaded to the Pi&#8217;s Broadcom VideoCore VII GPU via LiteRT&#8217;s WebGPU (Vulkan) backend through a component called ML Drift. The CPU delivers roughly 153.6 GFLOPS at FP32 precision; the GPU offers about 76.8 GFLOPS at the same precision but adds parallel execution that keeps the CPU free for higher-priority tasks. Supported GPU models include MediaPipe pipelines, Ultralytics YOLO object detection and Moonshine audio transcription.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Google also released a new LiteRT CLI tool that consolidates model conversion, quantization, benchmarking and inference into a single command set. The full workflow \u2014 from install to running Gemma 4 E2B \u2014 is designed to take just a few terminal commands. Separately, Google teased that LiteRT and Gemma support is coming soon to Hailo AI accelerators, including the Raspberry Pi AI HAT+ 2, an add-on board that would substantially accelerate inference beyond what the Pi&#8217;s built-in silicon can handle.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How This Fits Into a Crowded Field<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Edge AI runtimes are not a new category. Ollama has made it easy to run quantized models on consumer hardware, but it was designed primarily for desktop and server machines and runs llama.cpp under the hood \u2014 not purpose-built for the tight memory and thermal budgets of an IoT device. ONNX Runtime is generally faster than LiteRT&#8217;s predecessor (TensorFlow Lite) on the same Cortex-A76 silicon and is often the preferred choice for YOLO and PyTorch-origin models. Meta&#8217;s ExecuTorch and llama.cpp itself round out a runtime landscape that is genuinely competitive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Where LiteRT and Gemma 4 make a distinct argument is in native multimodal capability on constrained hardware. Gemma 4&#8217;s edge models handle audio and vision out of the box, without requiring separate pipeline components. Competing models in the same parameter range \u2014 Microsoft&#8217;s Phi-3 or Alibaba&#8217;s Qwen2.5, for instance \u2014 often outperform Gemma on pure text reasoning benchmarks but need additional models bolted on for audio and vision tasks. For developers building robotics or voice-first applications on a Pi, that integration simplifies the architecture considerably.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Students Should Pay Attention<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The practical implication for students and recent grads is straightforward: you can now build a portfolio-worthy AI project with hardware that costs less than a textbook. An offline voice assistant, a real-time object-detection robot, a local translation tool \u2014 any of these can run on a Raspberry Pi 5 with 8 GB of RAM, with no API fees and no data leaving the device. The open-source Gemma Translator repository and the Reachy Mini demo code, both linked from the Google announcement, provide concrete starting points rather than blank-page anxiety.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The LiteRT CLI also removes one of the most common stumbling blocks for beginners: environment setup. Consolidating conversion, quantization and inference into one tool means less time debugging dependency conflicts and more time actually building.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More broadly, the skills on display here \u2014 deploying AI at the edge, designing for memory and latency constraints, splitting workloads across CPU and GPU \u2014 are increasingly valued in embedded systems, robotics and IoT job roles. Building something that works offline, protects user privacy and operates under tight hardware limits is a different design challenge than calling an API, and employers working on devices that cannot rely on a cloud connection know the difference.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The upcoming Hailo HAT+ 2 integration is worth watching as well. If it delivers on Google&#8217;s claim of &#8220;massive hardware acceleration&#8221; through the same familiar LiteRT workflows, the performance ceiling for student projects on Pi hardware will rise significantly without requiring new code.<\/p>\n\n\n\n<div style=\"height:5px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"source-attribution wp-block-paragraph\"><strong>Source:<\/strong> <a href=\"https:\/\/developers.googleblog.com\/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma\/\" target=\"_blank\" rel=\"nofollow noopener\">Google<\/a><\/p>\n\n\n\n<details class=\"research-citations\">\n<summary>Additional research sources<\/summary>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/2606.05250\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/pdf\/2606.05250<\/a><\/li>\n<li><a href=\"https:\/\/www.raspberrypi.com\/news\/introducing-the-raspberry-pi-ai-hat-plus-2-generative-ai-on-raspberry-pi-5\/\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.raspberrypi.com\/news\/introducing-the-raspberry-pi-ai-hat-plus-2-generative-ai-on-raspberry-pi-5\/<\/a><\/li>\n<li><a href=\"https:\/\/www.mindstudio.ai\/blog\/gemma-4-edge-deployment-e2b-e4b-models\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.mindstudio.ai\/blog\/gemma-4-edge-deployment-e2b-e4b-models<\/a><\/li>\n<li><a href=\"https:\/\/datature.io\/blog\/gemma-4-what-computer-vision-engineers-actually-need-to-know\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/datature.io\/blog\/gemma-4-what-computer-vision-engineers-actually-need-to-know<\/a><\/li>\n<li><a href=\"https:\/\/mjrovai.github.io\/EdgeML_Made_Ease_ebook\/raspi\/litert-lm\/litert-lm.html\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/mjrovai.github.io\/EdgeML_Made_Ease_ebook\/raspi\/litert-lm\/litert-lm.html<\/a><\/li>\n<li><a href=\"https:\/\/mehmetozgenozdogan.medium.com\/raspberry-pi-5-as-an-edge-ai-platform-from-zero-to-local-llm-and-computer-vision-593ef686f17d\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/mehmetozgenozdogan.medium.com\/raspberry-pi-5-as-an-edge-ai-platform-from-zero-to-local-llm-and-computer-vision-593ef686f17d<\/a><\/li>\n<\/ul>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Google AI Edge has published a detailed guide showing how to deploy its Gemma 4 language model entirely offline on a Raspberry Pi 5 using LiteRT, its on-device inference runtime. The setup costs roughly $80 in hardware, requires no cloud connection, and hits generation speeds twice as fast as normal human speech.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[8],"tags":[3471,3501,3761,757,3763,764,746,3762,3462,3760,3211,3464],"class_list":["post-39385","post","type-post","status-publish","format-standard","hentry","category-ai","tag-alibaba","tag-edge-ai","tag-embedded-systems","tag-google","tag-hailo","tag-meta-ai","tag-microsoft","tag-moonshine-ai","tag-on-device-inference","tag-open-source-models","tag-robotics","tag-ultralytics"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.8 - aioseo.com -->\n\t<meta name=\"description\" content=\"Google AI Edge has published a detailed guide showing how to deploy its Gemma 4 language model entirely offline on a Raspberry Pi 5 using LiteRT, its on-device inference runtime. 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