Cache and reuse prompt prefixes with RadixAttention for LLM serving.
npx skills add https://github.com/m0at/claudemd --skill sglang-m0at
Please help me install this Agent Skill. Skill: sglang Source: https://github.com/m0at/claudemd/tree/main/skills/sglang Command: npx skills add https://github.com/m0at/claudemd --skill sglang-m0at
Slow, unstructured prompt processing and inefficient KV cache usage in LLM serving leads to high latency and limited throughput; RadixAttention addresses this by caching and reusing common prefixes across requests.
Install SGLang, start the server with your model path, and connect an OpenAI-compatible client to begin serving structured prompts at scale.
High-intent search queries and answers about installing and using this skill.
Reduce LLM serving latency by using RadixAttention to cache and reuse common prompt prefixes across multi-turn conversations. This mechanism optimizes KV cache usage, bypassing redundant processing for repeating inputs to improve throughput.
Enforce structured JSON or regex outputs from an OpenAI-compatible API by deploying a server that supports grammar-based generation. This ensures model responses strictly match your defined JSON schemas or regular expressions for production reliability.
Yes, you can use prefix caching for few-shot prompts and agent workflows. RadixAttention automatically identifies and reuses shared prefixes across these complex requests, accelerating structured generation at scale without manual cache management.
The best way to accelerate structured generation at scale is implementing an OpenAI-compatible API surface with RadixAttention. This caches common prefixes and supports JSON or grammar-based outputs, enabling efficient production deployments.
Throughput drops because unstructured prompt processing leads to inefficient KV cache usage and high latency. RadixAttention solves this by caching and reusing common prefixes across requests, optimizing processing for structured outputs.
syeda-hoorain-aliConfigure the OpenAI Agents SDK with a custom base URL and API key for alternative LLM providers.
air-gappedRoute OpenAI-compatible HTTP and gRPC traffic to vLLM/SGLang workers with policy-driven load balancing.
HemantSudarshanDesign structured prompts for LLMs with templates and testing workflows.
Supporter09Configure TensorRT-LLM inference with quantization, batching, and multi-GPU serving.
