Your GPU Is Lying to You About Its Capacity

This story was originally published on HackerNoon at: https://hackernoon.com/your-gpu-is-lying-to-you-about-its-capacity. A deep dive into KV cache fragmentation, PagedAttention, continuous batching, and the real bottlenecks behind production LLM inference. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #gpu-optimization, #llm-inference, #vllm, #transformer-architecture, #deep-learning, #ai-engineering, #mlops, #kv-cache, and more. This story was written by: @vineet-vijay. Learn more about this writer by checking @vineet-vijay's about page, and for more stories, please visit hackernoon.com. This article explores why production-grade LLM serving is fundamentally a memory management problem rather than a pure compute problem. Using real-world examples from GPU inference clusters, it breaks down KV cache fragmentation, PagedAttention, prefix caching, continuous batching, chunked prefill, speculative decoding, and KV cache quantization, showing how modern inference systems achieve massive throughput gains through smarter memory orchestration