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NVIDIA, MIT, and The University of Hong Kong Team Up to Launch Fast-dLLM Framework, Inference Speed Boosts Remarkably

NVIDIA and MIT Collaborate to Launch Fast-dLLM Framework, Boosting AI Inference Speed by 27.6 Times

Recently, tech giant NVIDIA, in collaboration with the Massachusetts Institute of Technology (MIT) and the University of Hong Kong, released a new framework called Fast-dLLM. This innovative framework aims to significantly increase the inference speed of diffusion models (Diffusion-based LLMs), up to 27.6 times, providing stronger technical support for AI applications. The challenges and opportunities of diffusion models make them strong competitors to traditional autoregressive models.

Brain-Computer Interface Clinical Enrollment Speeds Up in China to Tackle Paralysis and Aphasia

Meta Releases Multi-SpatialMLLM: Leading the Spatial Understanding Revolution in Multimodal AI

Qwen Lab and Peking University Release New Technology ZeroSearch, Lowering the Cost of LLM Retrieval Capability by 88%

Mistral Launches New Agents API: Empowering Developers to Build Intelligent AI Agents

Mistral recently launched its new Agents API, a framework designed for developers to simplify the creation of AI agents that can execute various tasks such as running Python code, generating images, and performing Retrieval-Augmented Generation (RAG). The launch of this API aims to provide a unified environment for large language models (LLMs) to interact with multiple tools and data sources in a structured and persistent manner.

Red Hat Joins Hands with Google and NVIDIA to Launch LLM-D Open Source Project to Solve Dual Challenges of Large-Scale AI Inference Cost and Latency

The global leader in open-source solutions, Red Hat, recently announced the launch of the revolutionary open-source project LLM-D, specifically addressing the urgent needs for large-scale inference in generative AI. The project, founded by industry giants such as CoreWeave, Google Cloud, IBM Research, and NVIDIA, aims to meet the most stringent production service-level goals through breakthrough technologies that enable cloud-based large language model inferencing.

MLX-LM Seamlessly Integrated with Hugging Face to Boost Efficient Large Language Model Performance on Apple Silicon Devices

Firecrawl Template Released! Turn Any Website into LLM-Ready Data with Just a Few Clicks and Speed Up AI Development by 10 Times!

ByteDance Unveils QuaDMix: A Unified Framework for Large Language Model Pre-training Data Quality and Diversity