Stop Wasting GPU Compute: Why SGLang is the Best Engine for LLM Agents
If you are running RAG pipelines, AI agents, or multi-turn chatbots on a dedicated GPU server, standard LLM serving engines are wasting your VRAM. In a standard setup, the key-value (KV) cache is discarded after every request. If your agent sends the same massive system prompt over and over, the engine recomputes it from scratch every time. Enter SGLang and RadixAttention. SGLang (developed out of UC Berkeley) solves this by organizing all cached KV states into a radix tree. It automatically finds the longest matching prefix and only computes attention for the new tokens. This drastically reduces the Time-to-First-Token (TTFT) for any workload with repeated context. Hardware VRAM Rule of Thumb for SGLang: Before deploying, you need to ensure your server has the right baseline: 8B Models (Llama 3.1): 16–24 GB VRAM (RTX 4090, A100 40GB) 32B Models: 48–64 GB VRAM (A100 80GB, H100) 70B Models: 140+ GB VRAM (Requires Tensor Parallelism across multiple GPUs) How to Deploy It Get...