​ This is a guest post co-written with Fred Wu from Sportradar. Sportradar is the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on… Read More »How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency Fred Wu AWS Machine Learning Blog

​ Posted by Thushan Ganegedara (GDE), Haidong Rong (Nvidia), Wei Wei (Google) Modern recommenders heavily leverage embeddings to create vector representations of each user and candidate item. These embedding can then be used to calculate the similarity between users and items, so that users are… Read More »Training a recommendation model with dynamic embeddings noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

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​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost