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NVIDIA’s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there’s a catch—one that reveals a fundamental tension in modern edge AI hardware design.
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After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)"><meta name=twitter:description content="Introduction Link to heading NVIDIA’s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there’s a catch—one that reveals a fundamental tension in modern edge AI hardware design.
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After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment."><meta property="og:url" content="https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)"><meta property="og:description" content="Introduction Link to heading NVIDIA’s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there’s a catch—one that reveals a fundamental tension in modern edge AI hardware design.
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After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-10-04T00:00:00+00:00"><meta property="article:modified_time" content="2025-10-04T20:41:50+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.4b392a85107b91dbdabc528edf014a6ab1a30cd44cafcd5325c8efe796794fca.css integrity="sha256-SzkqhRB7kdvavFKO3wFKarGjDNRMr81TJcjv55Z5T8o=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Why Your Jetson Orin Nano\u0027s 40 TOPS Goes Unused (And What That Means for Edge AI)","genre":"Blog","wordcount":"1866","url":"https:\/\/ericxliu.me\/posts\/benchmarking-llms-on-jetson-orin-nano\/","datePublished":"2025-10-04T00:00:00\u002b00:00","dateModified":"2025-10-04T20:41:50\u002b00:00","description":"\u003ch2 id=\u0022introduction\u0022\u003e\n Introduction\n \u003ca class=\u0022heading-link\u0022 href=\u0022#introduction\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h2\u003e\n\u003cp\u003eNVIDIA\u0026rsquo;s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there\u0026rsquo;s a catch—one that reveals a fundamental tension in modern edge AI hardware design.\u003c\/p\u003e\n\u003cp\u003eAfter running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device\u0026rsquo;s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn\u0026rsquo;t computation—it\u0026rsquo;s memory bandwidth. This isn\u0026rsquo;t just a quirk of one device; it\u0026rsquo;s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-10-04T00:00:00+00:00"><meta property="article:modified_time" content="2026-01-08T18:13:13+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.4b392a85107b91dbdabc528edf014a6ab1a30cd44cafcd5325c8efe796794fca.css integrity="sha256-SzkqhRB7kdvavFKO3wFKarGjDNRMr81TJcjv55Z5T8o=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"Why Your Jetson Orin Nano\u0027s 40 TOPS Goes Unused (And What That Means for Edge AI)","genre":"Blog","wordcount":"1866","url":"https:\/\/ericxliu.me\/posts\/benchmarking-llms-on-jetson-orin-nano\/","datePublished":"2025-10-04T00:00:00\u002b00:00","dateModified":"2026-01-08T18:13:13\u002b00:00","description":"\u003ch2 id=\u0022introduction\u0022\u003e\n Introduction\n \u003ca class=\u0022heading-link\u0022 href=\u0022#introduction\u0022\u003e\n \u003ci class=\u0022fa-solid fa-link\u0022 aria-hidden=\u0022true\u0022 title=\u0022Link to heading\u0022\u003e\u003c\/i\u003e\n \u003cspan class=\u0022sr-only\u0022\u003eLink to heading\u003c\/span\u003e\n \u003c\/a\u003e\n\u003c\/h2\u003e\n\u003cp\u003eNVIDIA\u0026rsquo;s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there\u0026rsquo;s a catch—one that reveals a fundamental tension in modern edge AI hardware design.\u003c\/p\u003e\n\u003cp\u003eAfter running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device\u0026rsquo;s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn\u0026rsquo;t computation—it\u0026rsquo;s memory bandwidth. This isn\u0026rsquo;t just a quirk of one device; it\u0026rsquo;s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/>Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
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<time datetime=2025-10-04T00:00:00Z>October 4, 2025
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@@ -25,17 +25,17 @@ After running 66 inference tests across seven different language models ranging
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<a class=heading-link href=#the-testing-process><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h3><p>Each model faced 10-12 prompts of varying complexity—from simple arithmetic to technical explanations about LLMs themselves. All tests ran with batch size = 1, simulating a single user interacting with a local chatbot—the typical edge deployment scenario. Out of 84 planned tests, 66 completed successfully (78.6% success rate). The failures? Mostly out-of-memory crashes on larger models and occasional inference engine instability.</p><h3 id=understanding-the-limits-roofline-analysis>Understanding the Limits: Roofline Analysis
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<a class=heading-link href=#understanding-the-limits-roofline-analysis><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h3><p>To understand where performance hits its ceiling, I applied roofline analysis—a method that reveals whether a workload is compute-bound (limited by processing power) or memory-bound (limited by data transfer speed). For each model, I calculated:</p><ul><li><strong>FLOPs per token</strong>: Approximately 2 × total_parameters (accounting for matrix multiplications in forward pass)</li><li><strong>Bytes per token</strong>: model_size × 1.1 (including 10% overhead for activations and KV cache)</li><li><strong>Operational Intensity (OI)</strong>: FLOPs per token / Bytes per token</li><li><strong>Theoretical performance</strong>: min(compute_limit, bandwidth_limit)</li></ul><p>The roofline model works by comparing a workload’s operational intensity (how many calculations you do per byte of data moved) against the device’s balance point. If your operational intensity is too low, you’re bottlenecked by memory bandwidth—and as we’ll see, that’s exactly what happens with LLM inference.</p><p><img src=/images/benchmarking-llms-on-jetson-orin-nano/16d64bdc9cf14b05b7c40c4718b8091b.png alt="S3 File"></p><h2 id=the-results-speed-and-efficiency>The Results: Speed and Efficiency
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<span class=sr-only>Link to heading</span></a></h3><p>To understand where performance hits its ceiling, I applied roofline analysis—a method that reveals whether a workload is compute-bound (limited by processing power) or memory-bound (limited by data transfer speed). For each model, I calculated:</p><ul><li><strong>FLOPs per token</strong>: Approximately 2 × total_parameters (accounting for matrix multiplications in forward pass)</li><li><strong>Bytes per token</strong>: model_size × 1.1 (including 10% overhead for activations and KV cache)</li><li><strong>Operational Intensity (OI)</strong>: FLOPs per token / Bytes per token</li><li><strong>Theoretical performance</strong>: min(compute_limit, bandwidth_limit)</li></ul><p>The roofline model works by comparing a workload’s operational intensity (how many calculations you do per byte of data moved) against the device’s balance point. If your operational intensity is too low, you’re bottlenecked by memory bandwidth—and as we’ll see, that’s exactly what happens with LLM inference.</p><p><img src="http://localhost:4998/attachments/image-79378d40267258c0d8968238cc62bd197dc894fa.png?client=default&bucket=obsidian" alt="S3 File"></p><h2 id=the-results-speed-and-efficiency>The Results: Speed and Efficiency
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<a class=heading-link href=#the-results-speed-and-efficiency><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h2><h3 id=what-actually-runs-fast>What Actually Runs Fast
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<a class=heading-link href=#what-actually-runs-fast><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h3><p>Here’s how the models ranked by token generation speed:</p><table><thead><tr><th>Rank</th><th>Model</th><th>Backend</th><th>Avg Speed (t/s)</th><th>Std Dev</th><th>Success Rate</th></tr></thead><tbody><tr><td>1</td><td>qwen3:0.6b</td><td>Ollama</td><td>38.84</td><td>1.42</td><td>100%</td></tr><tr><td>2</td><td>qwen2.5:0.5b</td><td>Ollama</td><td>35.24</td><td>2.72</td><td>100%</td></tr><tr><td>3</td><td>gemma3:1b</td><td>Ollama</td><td>26.33</td><td>2.56</td><td>100%</td></tr><tr><td>4</td><td>Qwen/Qwen2.5-0.5B-Instruct</td><td>vLLM</td><td>15.18</td><td>2.15</td><td>100%</td></tr><tr><td>5</td><td>Qwen/Qwen3-0.6B-FP8</td><td>vLLM</td><td>12.81</td><td>0.36</td><td>100%</td></tr><tr><td>6</td><td>gemma3n:e2b</td><td>Ollama</td><td>8.98</td><td>1.22</td><td>100%</td></tr><tr><td>7</td><td>google/gemma-3-1b-it</td><td>vLLM</td><td>4.59</td><td>1.52</td><td>100%</td></tr></tbody></table><p>The standout finding: quantized sub-1B models hit 25-40 tokens/second, with Ollama consistently outperforming vLLM by 2-6× thanks to aggressive quantization and edge-optimized execution. These numbers align well with independent benchmarks from NVIDIA’s Jetson AI Lab (Llama 3.2 3B at 27.7 t/s, SmolLM2 at 41 t/s), confirming this is typical performance for the hardware class.
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<img src=/images/benchmarking-llms-on-jetson-orin-nano/ee04876d75d247f9b27a647462555777.png alt="S3 File"></p><h3 id=responsiveness-first-token-latency>Responsiveness: First Token Latency
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<img src="http://localhost:4998/attachments/image-7913a54157c2f4b8d0b7f961640a9c359b2d2a4f.png?client=default&bucket=obsidian" alt="S3 File"></p><h3 id=responsiveness-first-token-latency>Responsiveness: First Token Latency
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<a class=heading-link href=#responsiveness-first-token-latency><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h3><p>The time to generate the first output token—a critical metric for interactive applications—varied significantly:</p><ul><li>qwen3:0.6b (Ollama): 0.522 seconds</li><li>gemma3:1b (Ollama): 1.000 seconds</li><li>qwen2.5:0.5b (Ollama): 1.415 seconds</li><li>gemma3n:e2b (Ollama): 1.998 seconds</li></ul><p>Smaller, quantized models get to that first token faster—exactly what you want for a chatbot or interactive assistant where perceived responsiveness matters as much as raw throughput.</p><h3 id=the-memory-bottleneck-revealed>The Memory Bottleneck Revealed
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<a class=heading-link href=#the-memory-bottleneck-revealed><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h3><p>When I compared actual performance against theoretical limits, the results were striking:</p><table><thead><tr><th>Model</th><th>Theoretical (t/s)</th><th>Actual (t/s)</th><th>Efficiency</th><th>Bottleneck</th><th>OI (FLOPs/byte)</th></tr></thead><tbody><tr><td>gemma3:1b</td><td>109.90</td><td>26.33</td><td>24.0%</td><td>Memory</td><td>3.23</td></tr><tr><td>qwen3:0.6b</td><td>103.03</td><td>38.84</td><td>37.7%</td><td>Memory</td><td>1.82</td></tr><tr><td>qwen2.5:0.5b</td><td>219.80</td><td>35.24</td><td>16.0%</td><td>Memory</td><td>3.23</td></tr><tr><td>gemma3n:e2b</td><td>54.95</td><td>8.98</td><td>16.3%</td><td>Memory</td><td>3.23</td></tr><tr><td>google/gemma-3-1b-it</td><td>30.91</td><td>4.59</td><td>14.9%</td><td>Memory</td><td>0.91</td></tr><tr><td>Qwen/Qwen3-0.6B-FP8</td><td>103.03</td><td>12.81</td><td>12.4%</td><td>Memory</td><td>1.82</td></tr><tr><td>Qwen/Qwen2.5-0.5B-Instruct</td><td>61.82</td><td>15.18</td><td>24.6%</td><td>Memory</td><td>0.91</td></tr></tbody></table><p>Every single model is memory-bound in this single-stream inference scenario. Average hardware efficiency sits at just 20.8%—meaning the computational units spend most of their time waiting for data rather than crunching numbers. That advertised 40 TOPS? Largely untapped when generating one token at a time for a single user.
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<img src=/images/benchmarking-llms-on-jetson-orin-nano/ee04876d75d247f9b27a647462555777.png alt="S3 File"></p><h2 id=what-this-actually-means>What This Actually Means
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<img src="http://localhost:4998/attachments/image-7913a54157c2f4b8d0b7f961640a9c359b2d2a4f.png?client=default&bucket=obsidian" alt="S3 File"></p><h2 id=what-this-actually-means>What This Actually Means
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<a class=heading-link href=#what-this-actually-means><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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<span class=sr-only>Link to heading</span></a></h2><h3 id=why-memory-bandwidth-dominates-in-single-stream-inference>Why Memory Bandwidth Dominates (in Single-Stream Inference)
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<a class=heading-link href=#why-memory-bandwidth-dominates-in-single-stream-inference><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
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@@ -62,4 +62,4 @@ After running 66 inference tests across seven different language models ranging
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2016 -
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2026
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