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+ <head>
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+ <meta charset="UTF-8">
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+ <title>The Bloatening: When AI Companies Forgot About the Little Guy | TinyMemoryLM</title>
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+ </style>
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+ </head>
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+ <body>
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+ <nav>
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+ <div class="container">
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+ <a href="index.html" class="nav-brand"><span>/</span>TinyMemoryLM</a>
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+ <div class="nav-links">
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+ <a href="index.html">Home</a>
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+ <a href="blog.html">Blog</a>
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+ <a href="status.html">Status</a>
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+ </div>
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+ </div>
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+ </nav>
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+ <main>
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+ <article class="post">
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+ <div class="container">
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+ <a href="blog.html" class="post-back">Back to Blog</a>
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+ <header>
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+ <div class="post-meta">
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+ <span class="post-date">2026-03-03</span>
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+ <span class="post-tag">Industry Rants</span>
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+ </div>
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+ <h1>The Bloatening: When AI Companies Forgot About the Little Guy</h1>
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+ </header>
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+ <div class="post-body">
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+ <p>I used to get excited about model releases. A new tiny model would drop and I would immediately try to run it on my laptop that sounds like a jet engine. Now I scroll through announcements and see numbers that require a data center just to pronounce.</p>
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+ <p>Take Qwen. I liked Qwen. Qwen had a 0.6B model. It was cute. It was manageable. It could run on hardware that did not require a second mortgage. Then came the next generation. Suddenly 0.6B became 0.8B. That is a thirty-three percent increase in bloat for one iteration. Meanwhile they are also releasing models with parameter counts that look like phone numbers.</p>
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+ <h2>The Creep of More</h2>
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+ <p>There is a pattern here. Start with something small and accessible. Build a community of developers who actually use your models on actual consumer hardware. Then quietly shift the goalposts. The new baseline is bigger. The new expectations require more VRAM. The new documentation assumes you have access to enterprise-grade infrastructure or at least a very understanding roommate with a powerful gaming rig.</p>
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+ <p>I am not saying bigger models are bad. Sometimes you need the big guns. But when every release nudges the minimum requirements upward, the little guys get left behind. People like me, who train models with 100K parameters for fun and profit - mostly fun, definitely not profit - start to feel like we are showing up to a Formula 1 race on a tricycle.</p>
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+ <blockquote>
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+ <p>Progress should not mean exclusion. Innovation should not require a hardware upgrade just to participate in the conversation.</p>
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+ </blockquote>
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+ <h2>My Tiny Rebellion</h2>
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+ <p>So I keep going small. I train models that fit in memory. I experiment with architectures that prioritize efficiency over scale. I accept that my models will sometimes respond to math questions with fish facts because they are tiny and confused and frankly adorable.</p>
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+ <p>Maybe this is stubborn. Maybe I am the one who needs to adapt. But I believe there is value in accessibility. There is power in letting people experiment without needing a cloud budget. There is something deeply human about building tools that regular people can actually use.</p>
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+ <p>Also my laptop is still making that jet engine noise and I cannot afford a wind tunnel.</p>
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+ <hr>
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+ </div>
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+ <footer class="post-footer">
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+ <p>Current status: Still training tiny models. Still getting fish answers. Still refusing to upgrade my hardware until absolutely necessary.</p>
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+ </footer>
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+ </div>
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+ </article>
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+ </main>
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+ <footer>
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+ <div class="container">
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+ <p>Built with curiosity over compute</p>
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+ <p>TinyMemoryLM by AILAY | 2026</p>
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+ </div>
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+ </footer>
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+ </body>
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+ </html>