AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, prompt processing, and autoregressive decoding to achieve certain model quality. An ideal model architecture should lower all above computation costs to facilitate scaling...
This work introduces Farseer, a novel and refined scaling law offering enhanced predictive accuracy across scales, and provides new insights into optimal compute allocation, better reflecting the nuanced demands of modern LLM training.
MISA-T, a routing-layer admission policy for mixed rollout serving that combines adaptive session admission, workload-aware KV-capacity allocation, and residency-time-aware KV accounting, is presented.
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