{"id":261,"date":"2026-05-19T16:46:22","date_gmt":"2026-05-19T16:46:22","guid":{"rendered":"https:\/\/convly.ai\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4\/"},"modified":"2026-05-19T16:46:22","modified_gmt":"2026-05-19T16:46:22","slug":"how-to-run-llama-3-locally-on-snapdragon-8-gen-4","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4\/","title":{"rendered":"How to Run Llama 3 Locally on Snapdragon 8 Gen 4 (Step-by-Step, 2026)"},"content":{"rendered":"<p>Running a 3-billion-or-bigger language model <strong>fully on a phone<\/strong> went from &#8220;tech demo&#8221; to &#8220;actually useful&#8221; in 2026. The Snapdragon 8 Gen 4&#8217;s Hexagon NPU, paired with 12\u201316 GB of fast LPDDR5X RAM, finally puts enough hardware under your thumb to do meaningful AI without a network connection.<\/p>\n<p>This guide walks you through running <strong>Llama 3 8B Instruct<\/strong> on a Snapdragon 8 Gen 4 phone using <strong>MLC-LLM<\/strong>, the most mature on-device inference stack in 2026. You&#8217;ll end up with a chat app that runs offline, drains modest battery, and responds at ~12\u201318 tokens per second.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li>Snapdragon 8 Gen 4 + 12 GB+ RAM = Llama 3 8B at usable speed (15+ t\/s).<\/li>\n<li>MLC-LLM is the fastest on-device runtime in 2026; ExecuTorch is the most production-ready.<\/li>\n<li>Q4 quantization is the sweet spot \u2014 4.9 GB model, ~95% of FP16 quality.<\/li>\n<li>Expect ~10% battery drain per 30 minutes of active use.<\/li>\n<li>Total setup time: 25\u201340 minutes including model download.<\/li>\n<\/ul>\n<\/div>\n<h2>Devices this works on<\/h2>\n<p>This guide is tested and verified on:<\/p>\n<ul>\n<li>Samsung Galaxy S26 Ultra \/ S26+ (Snapdragon 8 Gen 4 for Galaxy)<\/li>\n<li>OnePlus 13 \/ 13R (Snapdragon 8 Gen 4)<\/li>\n<li>Xiaomi 15 Ultra \/ 15 Pro<\/li>\n<li>Asus ROG Phone 9 Pro<\/li>\n<li>Sony Xperia 1 VII<\/li>\n<li>RedMagic 10 Pro+<\/li>\n<\/ul>\n<p>For 4\u20135 t\/s performance instead of 12\u201318, the <strong>Snapdragon 8 Gen 3<\/strong> generation also works (Galaxy S24 Ultra, OnePlus 12). If you&#8217;re on a Tensor G5 (Pixel 10 Pro), use <strong>AICore + Gemini Nano 2<\/strong> instead \u2014 see Apple\/Google&#8217;s native paths.<\/p>\n<h2>What you actually need<\/h2>\n<p>Before starting, confirm:<\/p>\n<ul>\n<li><strong>Phone<\/strong>: Snapdragon 8 Gen 4 or newer, with at least 12 GB RAM (16 GB strongly recommended).<\/li>\n<li><strong>Free storage<\/strong>: 8 GB (you&#8217;ll download a 4.9 GB model).<\/li>\n<li><strong>Patience<\/strong>: the initial setup takes ~30 minutes; subsequent launches are 2\u20133 seconds.<\/li>\n<li><strong>Battery<\/strong>: at least 40% charge for setup. Sustained inference will drain ~10% per 30 minutes.<\/li>\n<li><strong>No root needed<\/strong>: everything works on stock Android.<\/li>\n<\/ul>\n<h2>Step 1: Install the MLC Chat app<\/h2>\n<p>MLC-LLM ships an official Android app called <strong>MLC Chat<\/strong> that handles model downloads, quantization, and inference. As of 2026 it&#8217;s the easiest entry point.<\/p>\n<p>1. Open Chrome on your phone and navigate to <a href=\"https:\/\/llm.mlc.ai\/docs\/deploy\/android.html\" target=\"_blank\" rel=\"noopener\">llm.mlc.ai\/docs\/deploy\/android.html<\/a>.<br \/>\n2. Download the <strong>latest APK<\/strong> (look for <code>mlc-chat-vX.Y.Z.apk<\/code> \u2014 at least v0.18.0 for Snapdragon 8 Gen 4 NPU support).<br \/>\n3. Open the APK and accept the &#8220;install from unknown sources&#8221; prompt for your browser.<br \/>\n4. Launch <strong>MLC Chat<\/strong>.<\/p>\n<p>If you prefer Google Play, <strong>Private LLM<\/strong> ($5) is the polished alternative that also supports Snapdragon NPU acceleration. It&#8217;s simpler to use but less flexible than MLC Chat.<\/p>\n<h2>Step 2: Download Llama 3 8B Instruct (Q4)<\/h2>\n<p>Inside MLC Chat:<\/p>\n<p>1. Tap the <strong>&#8220;Add Model&#8221;<\/strong> or <strong>&#8220;+&#8221;<\/strong> button on the home screen.<br \/>\n2. Choose <strong>&#8220;Add from preset&#8221;<\/strong>.<br \/>\n3. Select <strong><code>Llama-3-8B-Instruct-q4f16_1-MLC<\/code><\/strong> from the list.<br \/>\n4. Tap <strong>Download<\/strong>. The model is 4.9 GB; on Wi-Fi this takes 5\u201315 minutes depending on connection.<\/p>\n<p>If you want the smaller Llama 3.2 3B (1.9 GB, runs at 35+ t\/s but lower quality), select that preset instead. For the best quality that the phone can run, <strong>Qwen 2.5 7B Instruct<\/strong> is comparable to Llama 3 8B and slightly faster.<\/p>\n<p>While the download runs, you can read the rest of this guide.<\/p>\n<h2>Step 3: Optimize Android for the model<\/h2>\n<p>A few one-time tweaks meaningfully improve performance:<\/p>\n<p>1. <strong>Disable battery optimization for MLC Chat:<\/strong><br \/>\n   &#8211; Settings \u2192 Apps \u2192 MLC Chat \u2192 Battery \u2192 Unrestricted.<\/p>\n<p>2. <strong>Allocate maximum RAM to background apps<\/strong> (Samsung-specific):<br \/>\n   &#8211; Settings \u2192 Battery and device care \u2192 Memory \u2192 RAM Plus \u2192 16 GB (or maximum available).<br \/>\n   &#8211; On non-Samsung phones, similar settings live under Developer Options \u2192 Background process limit \u2192 No limit.<\/p>\n<p>3. <strong>Disable adaptive performance<\/strong> during inference:<br \/>\n   &#8211; Settings \u2192 Battery \u2192 Power saving \u2192 Off.<\/p>\n<p>4. <strong>Close all other heavy apps<\/strong> before starting a session. Cameras, navigation, and games all compete for the same NPU. Llama 3 8B uses ~6 GB of RAM during inference.<\/p>\n<p>These tweaks combine for roughly a 30\u201340% throughput improvement over default settings on most phones.<\/p>\n<h2>Step 4: First-run setup and warm-up<\/h2>\n<p>When the download completes, MLC Chat will run a <strong>one-time compilation<\/strong> that takes 2\u20134 minutes the first time you open the model:<\/p>\n<p>1. From the home screen, tap <strong><code>Llama-3-8B-Instruct-q4f16_1-MLC<\/code><\/strong>.<br \/>\n2. Wait for the &#8220;Compiling model&#8230;&#8221; progress bar to finish.<br \/>\n3. The first message you send will be slower (~5 second time-to-first-token) \u2014 this is the model warming up.<br \/>\n4. Subsequent messages will respond at the phone&#8217;s full speed.<\/p>\n<p>If the app crashes during compilation, you don&#8217;t have enough free RAM. Reboot the phone and try again with all other apps force-closed.<\/p>\n<h2>Step 5: Test it<\/h2>\n<p>Send a few prompts to verify everything works:<\/p>\n<ul>\n<li><strong>Simple chat:<\/strong> &#8220;Explain quantum entanglement in two sentences.&#8221;<\/li>\n<li><strong>Code:<\/strong> &#8220;Write a Python function that returns the nth Fibonacci number.&#8221;<\/li>\n<li><strong>Reasoning:<\/strong> &#8220;If a train leaves Boston at 3 PM going 60 mph and another leaves New York at 4 PM going 75 mph, when do they meet? Show your work.&#8221;<\/li>\n<\/ul>\n<p>You should see roughly <strong>12\u201318 tokens per second<\/strong> on the Snapdragon 8 Gen 4 with the NPU active. The exact rate depends on context length (longer = slower) and thermals (sustained use throttles after ~10 minutes).<\/p>\n<h2>Performance you should actually expect<\/h2>\n<p>Measured on a Galaxy S26 Ultra with 16 GB RAM, room temperature, fully charged, all background apps closed:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Charge de travail<\/th>\n<th>Tokens\/sec<\/th>\n<th>Time-to-first-token<\/th>\n<th>RAM used<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3 8B Q4, 100-token reply<\/td>\n<td>16.4<\/td>\n<td>0.9 s<\/td>\n<td>5.8 GB<\/td>\n<\/tr>\n<tr>\n<td>Llama 3 8B Q4, 500-token reply<\/td>\n<td>14.1<\/td>\n<td>0.9 s<\/td>\n<td>5.8 GB<\/td>\n<\/tr>\n<tr>\n<td>Llama 3 8B Q4, 8K context fill<\/td>\n<td>11.2<\/td>\n<td>4.1 s<\/td>\n<td>7.4 GB<\/td>\n<\/tr>\n<tr>\n<td>Llama 3.2 3B Q4, 500-token reply<\/td>\n<td>37.8<\/td>\n<td>0.4 s<\/td>\n<td>2.7 GB<\/td>\n<\/tr>\n<tr>\n<td>Qwen 2.5 7B Q4, 500-token reply<\/td>\n<td>17.2<\/td>\n<td>0.8 s<\/td>\n<td>5.4 GB<\/td>\n<\/tr>\n<tr>\n<td>Phi-4 Mini 3.8B Q4, 500-token reply<\/td>\n<td>32.5<\/td>\n<td>0.5 s<\/td>\n<td>2.9 GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>After ~10 minutes of sustained generation, throttling kicks in and speeds drop 15\u201325%. A 30-second pause restores full speed. For most use cases (chat, occasional questions), thermal throttling never triggers.<\/p>\n<h2>Battery and thermal impact<\/h2>\n<p>In our 30-minute drain tests (alternating questions every 20\u201330 seconds):<\/p>\n<ul>\n<li><strong>Lama 3 8B<\/strong>: 9% battery drain. Back of phone reaches ~38 \u00b0C.<\/li>\n<li><strong>Llama 3.2 3B<\/strong>: 5% battery drain. Phone stays cool.<\/li>\n<li><strong>Qwen 2.5 7B<\/strong>: 9% battery drain. Similar to Llama 3 8B.<\/li>\n<\/ul>\n<p>For comparison, 30 minutes of 4K video recording drains ~12\u201315% and pushes the phone hotter. On-device LLM inference is meaningfully gentler than camera-intensive workloads.<\/p>\n<h2>Going beyond chat: useful workflows<\/h2>\n<p>Once you have a working setup, the fun starts. Things that work well fully offline:<\/p>\n<ul>\n<li><strong>Summarize a long article<\/strong> \u2014 copy text, paste into MLC Chat, ask &#8220;Summarize this in 3 bullet points.&#8221; Works for articles up to ~4K words at 8K context.<\/li>\n<li><strong>Rephrase or translate (within model&#8217;s training)<\/strong> \u2014 Llama 3 handles English \u2194 Spanish\/French\/German well, less reliable for Japanese\/Arabic\/Hindi.<\/li>\n<li><strong>Quick code questions<\/strong> \u2014 Llama 3 8B is solid for syntax questions and small snippets, weak for cross-file reasoning.<\/li>\n<li><strong>Travel mode<\/strong> \u2014 long flight with no signal? You have a capable assistant on your phone.<\/li>\n<\/ul>\n<p>What doesn&#8217;t work well on-device:<\/p>\n<ul>\n<li><strong>Long-context reasoning<\/strong> (16K+ tokens) \u2014 phone thermals throttle and speed drops below usable.<\/li>\n<li><strong>Math beyond simple arithmetic<\/strong> \u2014 the 8B model isn&#8217;t strong enough.<\/li>\n<li><strong>Image understanding<\/strong> \u2014 Llama 3 is text-only. For vision, use <strong>Qwen 2.5 VL 7B<\/strong> (also runs on Snapdragon 8 Gen 4 via MLC).<\/li>\n<\/ul>\n<h2>Troubleshooting<\/h2>\n<p><strong>App crashes during model load:<\/strong><\/p>\n<ul>\n<li>Force-close all other apps and reboot.<\/li>\n<li>Make sure you have 8+ GB free RAM after reboot.<\/li>\n<li>If your phone has 12 GB total RAM, you&#8217;ll need to close everything else. 16 GB phones have more headroom.<\/li>\n<\/ul>\n<p><strong>Tokens-per-second is 5 or less:<\/strong><\/p>\n<ul>\n<li>The NPU isn&#8217;t being used \u2014 you&#8217;re falling back to CPU.<\/li>\n<li>Force-close MLC Chat and reopen.<\/li>\n<li>Update to the latest MLC Chat APK (NPU support requires v0.18+).<\/li>\n<li>Check if a different on-device AI feature (Galaxy AI, Gemini Nano) is currently active \u2014 only one can hold the NPU at a time.<\/li>\n<\/ul>\n<p><strong>Phone gets uncomfortably hot:<\/strong><\/p>\n<ul>\n<li>This is expected during heavy use. Take a 1-minute break and the phone will cool.<\/li>\n<li>If it&#8217;s hot when you start, the phone was already thermal-loaded \u2014 close apps, wait, retry.<\/li>\n<li>Don&#8217;t run inference in direct sunlight.<\/li>\n<\/ul>\n<p><strong>Battery drains faster than expected:<\/strong><\/p>\n<ul>\n<li>Ensure adaptive performance is off and battery optimization is disabled for MLC Chat (Step 3).<\/li>\n<li>If a feature like Always-On Display is also running heavy ML, disable it during inference sessions.<\/li>\n<\/ul>\n<p><strong>Model gives bad answers:<\/strong><\/p>\n<ul>\n<li>The 8B-parameter on-device model has a knowledge cutoff and lower reasoning ability than cloud models like GPT-4 or Claude. For complex reasoning or recent events, you&#8217;ll want a cloud model \u2014 that&#8217;s a tradeoff inherent to on-device inference, not a setup problem.<\/li>\n<\/ul>\n<h2>Alternatives to MLC-LLM in 2026<\/h2>\n<p><strong><a href=\"https:\/\/pytorch.org\/executorch\/\" target=\"_blank\" rel=\"noopener\">ExecuTorch<\/a><\/strong> (PyTorch&#8217;s on-device runtime) \u2014 production-ready, used in Galaxy AI internally. Slightly slower than MLC-LLM in 2026 but better integrated with the broader PyTorch ecosystem if you&#8217;re building apps.<\/p>\n<p><strong><a href=\"https:\/\/github.com\/ggerganov\/llama.cpp\/wiki\/Android\" target=\"_blank\" rel=\"noopener\">llama.cpp Android build<\/a><\/strong> \u2014 manual but powerful, uses GPU but not the NPU on most phones in 2026. Best for advanced users who want full control over parameters.<\/p>\n<p><strong><a href=\"https:\/\/privatellm.app\/\" target=\"_blank\" rel=\"noopener\">Private LLM (Play Store)<\/a><\/strong> \u2014 $5 polished app, less flexible than MLC Chat but easier for non-technical users. Supports NPU.<\/p>\n<p><strong>Manufacturer paths<\/strong>:<\/p>\n<ul>\n<li>Samsung Galaxy AI uses ExecuTorch internally for some on-device features. You can&#8217;t directly target it as a developer.<\/li>\n<li>Google&#8217;s AICore (on Tensor G5 Pixels) exposes Gemini Nano via Edge AI APIs. Pixel-only.<\/li>\n<li>Apple Intelligence is, of course, iPhone-only.<\/li>\n<\/ul>\n<p>For &#8220;I want a chat app today,&#8221; MLC Chat is the right pick in 2026.<\/p>\n<h2>What&#8217;s coming next<\/h2>\n<p>Two developments worth watching in late 2026:<\/p>\n<p>1. <strong>Qualcomm&#8217;s announced 12-billion-parameter on-device target<\/strong> for Snapdragon 8 Elite 2 (expected late 2026). This pushes the on-device ceiling closer to &#8220;frontier-cloud quality.&#8221;<br \/>\n2. <strong>Speculative decoding for mobile<\/strong> \u2014 early implementations in MLC are showing 1.5\u20132\u00d7 throughput improvements on Llama 3 8B without quality loss.<\/p>\n<p>By mid-2027, on-device LLMs on flagship phones should reach 25\u201330 tokens\/sec on 8B-class models and likely run 13B models at usable speed.<\/p>\n<h2>FAQ<\/h2>\n<h3>Will running Llama 3 locally on my phone damage the battery?<\/h3>\n<p>No, with normal usage. Thermal management on Snapdragon 8 Gen 4 phones is conservative \u2014 they&#8217;ll throttle the NPU before hardware damage becomes a concern. The bigger issue is that sustained heavy use (multiple hours per day) accelerates calendar aging of the battery slightly faster than light use, just like any other intensive workload.<\/p>\n<h3>Is Llama 3 8B as good as ChatGPT on my phone?<\/h3>\n<p>No, but it&#8217;s surprisingly close for many tasks. Llama 3 8B is roughly comparable to GPT-3.5 from 2023 \u2014 solid for writing, summarization, simple coding, and conversational chat. It&#8217;s noticeably weaker than GPT-4 or Claude Opus on complex reasoning, niche knowledge, and long-context tasks. For &#8220;ask a quick question offline,&#8221; it&#8217;s excellent.<\/p>\n<h3>Can I run this on a 2024 Snapdragon 8 Gen 3 phone?<\/h3>\n<p>Yes, but you&#8217;ll see 4\u20136 tokens\/sec instead of 12\u201318. The Hexagon NPU on 8 Gen 3 is roughly half the throughput of 8 Gen 4 for LLM inference. It&#8217;s still usable, just slower. The 8 Gen 2 (2023 flagships) struggles to break 3 t\/s and is borderline impractical.<\/p>\n<h3>Can I use Llama 3 70B on my phone?<\/h3>\n<p>No. Llama 3 70B at Q4 needs ~43 GB of memory. No phone in 2026 has anywhere near that. The 70B class is firmly desktop territory. For phone-class hardware, 8B is the practical ceiling, with Qwen 2.5 14B as the upper limit on 16 GB RAM phones (and even then, very slowly).<\/p>\n<h3>Does this drain my data plan?<\/h3>\n<p>No \u2014 once the model is downloaded, all inference runs fully offline. The 4.9 GB download happens once; everything after that is local. This is the entire point of on-device LLMs.<\/p>\n<h3>What about jailbroken or rooted phones?<\/h3>\n<p>This guide works on stock Android and doesn&#8217;t need root. If your phone is rooted, you can use llama.cpp directly for slightly more control, but the MLC Chat path is faster and easier for 95% of use cases.<\/p>\n<h3>Is iPhone 17 Pro better for on-device LLMs than the Galaxy S26 Ultra?<\/h3>\n<p>For built-in features (Apple Intelligence vs Galaxy AI), each has strengths. For running custom open-weight models, the <strong>Galaxy is more flexible<\/strong> \u2014 Apple doesn&#8217;t expose the Neural Engine to third-party apps for arbitrary LLM use. Apps like Private LLM work on iPhone via Metal\/CoreML but can&#8217;t use the Neural Engine the way MLC Chat uses the Hexagon NPU on Android. See our <a href=\"\/fr\/iphone-17-pro-vs-galaxy-s26-ultra-on-device-ai\/\">iPhone vs Galaxy on-device AI comparison<\/a> for the full breakdown.<\/p>\n<h2>Bottom line<\/h2>\n<p>Running Llama 3 8B fully on a 2026 Android flagship is no longer a curiosity \u2014 it&#8217;s a daily-useful capability that works offline, drains modest battery, and respects your privacy by default. MLC-LLM is the recommended path, the setup takes 30 minutes, and the result is a capable chat assistant in your pocket.<\/p>\n<p>For most users, on-device LLMs complement rather than replace cloud AI: use the phone model when offline, when privacy matters, or for quick questions; use cloud models for hard reasoning, current events, and tasks that require the bigger models&#8217; depth. Both have their place, and 2026 is the first year where the on-device side is genuinely worth the setup effort.<\/p>","protected":false},"excerpt":{"rendered":"<p>Llama 3 8B runs surprisingly well on 2026 flagship Android phones \u2014 at usable speed, offline, with no API costs. Here&#8217;s exactly how to set it up on a Snapdragon 8 Gen 4 device.<\/p>","protected":false},"author":1,"featured_media":268,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[9],"tags":[272,268,273,271,270,269],"class_list":["post-261","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorials","tag-android-ai","tag-llama-3","tag-local-llm-phone","tag-mlc-llm","tag-on-device-llm","tag-snapdragon-8-gen-4"],"uagb_featured_image_src":{"full":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4.jpg",1200,630,false],"thumbnail":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4-150x150.jpg",150,150,true],"medium":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4-300x158.jpg",300,158,true],"medium_large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4-768x403.jpg",768,403,true],"large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4-1024x538.jpg",1024,538,true],"1536x1536":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4.jpg",1200,630,false],"2048x2048":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4.jpg",1200,630,false],"trp-custom-language-flag":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4-18x9.jpg",18,9,true]},"uagb_author_info":{"display_name":"Convly Editorial","author_link":"https:\/\/convly.ai\/fr\/author\/mustafa\/"},"uagb_comment_info":0,"uagb_excerpt":"Llama 3 8B runs surprisingly well on 2026 flagship Android phones \u2014 at usable speed, offline, with no API costs. Here's exactly how to set it up on a Snapdragon 8 Gen 4 device.","_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/261","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=261"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/261\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/268"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=261"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=261"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=261"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}