This paper develops a method to scale up language models by increasing their memory capacity, allowing for better performance and more efficient use of parameters. Practitioners may care about this research as it could lead to more powerful and efficient language models for applications like language translation and text generation.
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Reiner Pope delivers a blackboard lecture on the mathematical and hardware principles behind training and serving large language models. He explains how batch size, sparsity, and various parallelism strategies (expert, pipeline) impact late…
LLM trainingLLM inferenceBatch sizeLatency optimizationCost analysisRoofline analysisMemory bandwidthCompute performanceKV CacheSparsityMixture of ExpertsExpert parallelismData center architectureScale up networkScale out networkPipeline parallelismMicrobatchingMemory capacityChinchilla scalingRL generationAPI pricingContext lengthCryptographic ciphersNeural network architectureReversible networks