A two-person team integrated the SGLang serving stack with IBM's Spyre inference accelerator in less than a week using AI coding agents, producing a plugin of roughly 1,500 lines of code, according to a PyTorch blog post1. SGLang delivered about 3.6× the throughput of the reference PyTorch inference implementation using torch-spyre. Performance was on par with vLLM on workloads measured for Granite 3.3 8B and Gemma 4 26B A4B. RadixCache prefix sharing worked without any Spyre-specific changes.
IBM Spyre Integrated into SGLang in Under a Week with AI Coding Agents
A two-person team used AI coding agents to integrate SGLang with IBM's Spyre accelerator in under a week, achieving 3.6× throughput over the reference PyTorch implementation.
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