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AIGoogle Cloud Blogabout 3 hours ago
Co-operative time-slicing boosts GPU utilizationA new co-operative time-slicing solution from the llm-d project interleaves independent reinforcement learning jobs onto shared hardware. Benchmarks show aggregate accelerator duty cycles rising from roughly 40% to 70% without affecting model convergence. The approach eliminates idle GPU time during RL post-training phases like sampling and gradient updates.
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