SGLang ported to IBM Spyre in under a week via AI coding agents, 3.6x throughput
PyTorch · x · 2026-11-12
PyTorch announces that, with help from AI coding agents, SGLang was integrated with IBM's Spyre accelerator in under a week using only a 1,500-line plugin. Reusing existing PyTorch Spyre support and SGLang extension points delivered roughly 3.6x the throughput of the reference torch-spyre implementation.
Performance matched vLLM on Granite 3.3 8B (relative 0.993) and Gemma 4 26B-A4B (1.008) workloads, with RadixCache prefix sharing working out of the box with no hardware-specific changes. PyTorch frames this as proof that open standards and modular architectures let developers bring new silicon online quickly.
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