GPU Hunter summary of 2504.19720, focused on what this paper means for local AI inference, quantization, serving behavior, and hardware choice.
A broad survey of efficient LLM serving methods covering memory overhead, attention costs, batching, quantization, and system design. It gives readers a current map before diving into specialized serving, kernel, and KV-cache papers.
This is the best general map for visitors moving from personal local inference to a shared GPU server or internal tool.
It gives readers a current map before diving into specialized serving, kernel, and KV-cache papers. The useful part for GPU Hunter readers is not the abstract result alone; it is the hardware implication: whether a model fits, whether a runtime can use the format, or whether throughput is limited by memory movement instead of arithmetic.
Use this as a 2025 baseline for the serving stack before comparing vLLM, SGLang, TensorRT-LLM, and local runtimes. For shared inference, the important question is not only how fast one prompt runs. Batching, scheduling, cache placement, and request mix decide whether a GPU behaves like a reliable service.
Use this paper when comparing GeForce RTX 4090, RTX PRO 6000 Blackwell, Radeon RX 7900 XTX. Serving workloads need enough VRAM, strong bandwidth, and runtime features that survive batching and concurrency.
This page is GPU Hunter editorial context and does not reproduce the paper abstract. Use the original arXiv, PDF, and Hugging Face links for the complete paper text and author-provided details.