Categories: FAANG

Accelerating LLM Inference on NVIDIA GPUs with ReDrafter

Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used for production applications across the industry.
Earlier this year, we published and open sourced Recurrent Drafter (ReDrafter), a novel approach to speculative decoding that achieves state of the art…
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Having Fun with Ai

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Datasets for Training a Language Model

A good language model should learn correct language usage, free of biases and errors.

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Everyone can now fly their own drone.

TL;DR Using Google’s new Veo 3.1 video model, we created a breathtaking 1 minute 40…

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CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching

Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs.…

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Announcing BigQuery-managed AI functions for better SQL

For decades, SQL has been the universal language for data analysis, offering access to analytics…

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