Hyperloom: A Multi-Agent Harness for Autonomous Inference Optimization on AMD GPUs

AMD released Hyperloom, an open-source system designed to improve inference performance on Instinct GPUs. The tool uses a multi-agent structure to profile workloads and identify improvements in frameworks and kernels. It operates without human intervention, validating every change to ensure stability. AMD reports that the system achieved a median speedup of 1.73x during unattended evaluations. Traditional optimization requires significant time from specialized engineers for each new model or hardware update. Hyperloom automates this loop, allowing teams to optimize over 14,000 models without manual tuning for every instance. The tool supports vLLM, SGLang, and xDiT frameworks, covering text and image generation tasks. By carrying forward proven results, the system aims to reduce the ongoing labor costs associated with maintaining high-efficiency AI serving stacks. The full range of performance gains reported by AMD extended from 1.35x to 7.31x, though individual results vary by workload. The authors attribute previous failures in automated optimization to model drift, lack of memory, and unsafe actions. They argue that prompt engineering alone cannot fix these issues, which is why a dedicated harness was necessary. The specific details of the internal recipe knowledge base structure remain summarized rather than fully detailed in this text.