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Morning Edition · Wednesday, July 29, 2026Published at 1:45 AM EDT · New York
Kernel Forge Puts an LLM Agent to Work Writing and Optimizing CUDA Kernels
The harness targets the small set of compute kernels, matrix multiply, convolution, and normalization, where machine-learning runtime is actually spent.
A paper titled "Kernel Forge" presents an agent harness for LLM-based generation and optimization of CUDA kernels. The premise is practical. Most runtime in machine-learning workloads occurs in a small set of compute kernels such as matrix…
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Part of a tracked trend
The Inference-Cost Efficiency Race
Techniques that cut tokens generated and KV-cache memory per query will keep compressing the marginal cost of serving reasoning models, making inference efficiency a recurring competitive axis alongside raw capability.
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