Serving Large Language Models (LLMs) efficiently requires more than just throwing GPUs at the problem. If you are dealing with sluggish inference, you are likely suffering from VRAM fragmentation and poor batching optimization.
In this tutorial, we cover how to deploy SGLang on a bare-metal server to solve these issues.
Key Takeaways:
SGLang Architecture: Understand how SGLang handles KV cache and request batching to eliminate memory waste.
Multi-Model Serving: Learn the configuration required to run multiple LLMs concurrently on the same GPU without resource contention.
Deployment Steps: A step-by-step guide to getting the environment up and running on a raw Linux machine for zero hypervisor overhead.
Stop wasting your compute cycles. Read the full guide here:
https://www.idatam.com/tutorials/howto/deploy-sglang-multi-model-gpu-server/
If you need a reliable, unvirtualized environment to test this stack, check out our line of bare-metal Dedicated Servers:
https://www.idatam.com/dedicated-servers/
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