Model Compression: Reduce LLM Size Without Losing Performance

When you run a large language model in production, you’re not just paying for the AI—you’re paying for its memory footprint, the amount of RAM and storage a model needs to operate. Also known as model size optimization, it’s the difference between a model that runs on a single GPU and one that needs a cluster of servers. Most LLMs today are bloated. A 70-billion-parameter model might use over 140GB of memory just to load. That’s not just expensive—it’s unsustainable. Quantization, reducing the precision of model weights from 32-bit to 8-bit or even 4-bit. Also known as weight compression, it cuts memory use by up to 75% with almost no drop in accuracy. And it’s not just about weights. The KV cache, the temporary memory storing past attention outputs during generation. Also known as key-value cache, it now often takes up more space than the model itself in long conversations. If you’re running chatbots or document summarizers, the KV cache is your biggest cost driver.

That’s where FlashAttention, a faster, memory-efficient way to compute attention that reduces cache size and speeds up inference. Also known as optimized attention, it’s not magic—it’s math. Companies like Hugging Face and Anthropic use it to cut latency by 30% while using less GPU memory. Then there’s pruning, removing redundant neurons or connections that don’t contribute to output quality. Also known as weight pruning, it’s like trimming a tree to let the strongest branches grow. Combine that with prompt compression, shortening input text without losing meaning. Also known as input token reduction, and you’re not just shrinking the model—you’re shrinking everything around it. These aren’t theoretical tricks. They’re the reason some teams run LLMs on laptops, why customer service bots respond in under 500ms, and why cloud bills dropped 60% last year for companies that didn’t just upgrade hardware—they optimized software.

What you’ll find below isn’t theory. It’s real-world fixes. Posts show how teams cut token costs by 80%, why FlashAttention beats older attention methods, how quantization affects hallucination rates, and when skipping model compression actually costs more. You’ll see exactly which techniques work for small teams, which need enterprise infrastructure, and which are just hype. No fluff. Just what moves the needle on cost, speed, and reliability.