How to Stop Wasting GPU Cycles When Serving LLMs

Multi-Model Serving with SGLang on Dedicated GPUs

The demand for Large Language Models (LLMs) is skyrocketing, but the cost of the hardware required to run them is a significant barrier. Many developers assume they need to keep buying more GPUs to scale, but the truth is, most AI deployments are simply inefficient.

VRAM fragmentation and poor request batching lead to wasted GPU cycles and sluggish inference. The solution lies in better serving frameworks, specifically SGLang.

In our latest guide, we explain how deploying SGLang on a bare-metal GPU server allows you to serve multiple LLMs highly efficiently. SGLang optimizes memory allocation (like RadixAttention) to ensure your GPU’s VRAM is fully utilized, meaning faster response times and higher throughput without upgrading your hardware.

Read the full technical walkthrough on iDatam: https://www.idatam.com/tutorials/howto/deploy-sglang-multi-model-gpu-server/

If your current infrastructure is bottlenecking your AI ambitions, it might be time to upgrade your metal. Explore iDatam’s robust Dedicated Servers to find the perfect foundation for your LLMs: https://www.idatam.com/dedicated-servers/