IBM two-person team integrates SGLang into Spyre in one week with AI agents
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A two-person IBM team, with the help of AI coding agents, integrated SGLang into the IBM Spyre accelerator in under a week, building a plugin of about 1,500 lines of code. The plugin has been open-sourced along with upstream SGLang. According to a PyTorch Blog report, SGLang on Spyre delivers about 3.6 times the throughput of the torch-spyre reference PyTorch inference implementation, and matches vLLM on measured workloads with Granite 3.3 8B and Gemma 4 26B A4B. The usage examples assume that the Spyre runtime, torch-spyre, the SGLang plugin, and their compatibility patches are already installed and configured.
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- PyTorch BlogIntegrating SGLang into Spyre in Under a Week with AI
A two-person team used AI coding agents to integrate SGLang with IBM Spyre in under a week, producing a plugin of roughly 1,500 lines built on existing PyTorch Spyre support and SGLang's extension points. SGLang delivered about 3.6× the throughput of the reference PyTorch inference implementation using torch-spyre and was on par with vLLM on the measured workloads for Granite 3.3 8B and Gemma 4 26B A4B. Prefix sharing through RadixCache worked without any Spyre-specific changes.
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