MegaT model launched on PyPI Achieving 400 tokens/second ultra-fast response

MiTA AI Search Launches New Ultra-Fast Model: Up to 400 tokens/second response speed

Recently, MiTA AI Search officially launched the new ultra-fast model, bringing users a more efficient and accurate search experience. The MiTA AI Search team successfully achieved a maximum response speed of 400 tokens/second on a single H800 GPU through kernel fusion technology on GPUs and dynamic compilation optimization strategies on CPUs, with most responses provided within 2 seconds.

ByteDance Releases MegaTTS3 on Hugging Face: A Breakthrough in Lightweight Speech Synthesis

Beijing—ByteDance recently released its latest text-to-speech (TTS) model, MegaTTS3, on the Hugging Face open-source AI community. This release has quickly garnered attention from AI researchers and developers worldwide due to its breakthroughs in lightweight design and multilingual support. Based on community feedback and official information, MegaTTS3 is hailed as a significant advancement in speech synthesis. MegaTTS3's core highlights are...

Moore Threads Open-Sources Two Major AI Frameworks, Achieving Over 90% Training Efficiency on Domestic GPUs

Korean VESSL AI Secures $12 Million Funding to Reduce Enterprise GPU Costs by Up to 80%

As artificial intelligence increasingly integrates into enterprise workflows and products, the demand for Machine Learning Operations (MLOps) platforms is rising. These platforms assist companies in creating, testing, and deploying machine learning models more easily. However, despite the presence of several competitors in the market, such as startups like InfuseAIComet and major companies like Google Cloud, Azure, and AWS, South Korea's VESSL AI aims to carve out its niche by focusing on optimizing GPU costs.

New Breakthrough in GPU Optimization! 'Tree Attention' Accelerates Inference of 5 Million Long Texts by 8 Times

The Transformer architecture, a star in the field of artificial intelligence, has led a revolution in natural language processing with its self-attention mechanism at its core. However, when handling long contexts, the resource consumption of self-attention calculations becomes a bottleneck. To address this issue, researchers have proposed the Tree Attention method, which decomposes the calculation tasks through tree reduction to improve efficiency. This method not only reduces communication overhead and memory usage but is also 8 times faster than existing methods in a multi-GPU environment. The introduction of Tree Attention not only enhances performance.

Nvidia Unveils New Megaton B200! H100 Single Machine Can Train 15 GPT-4 Models, AI Welcomes New Moore's Era

Nvidia has launched the new Megaton B200, capable of training 15 GPT-4 models as we embrace the new Moore's Era. The Blackwell B200 GPU offers powerful performance, accelerating inference by 30 times while reducing cost and energy consumption by 25 times. The combination of Blackwell chips with the Grace CPU in the GB200 provides efficient inference capabilities. The new GB200 chip supports the second generation of Transformer engines, showing significant performance enhancements. NVLink networking technology delivers higher performance, achieving groundbreaking advancements in AI model training.

Byte's MegaScale System Successfully Built for Large-Scale Card Clusters, Efficiently Completing GPT-3 Training

Byte successfully built a card cluster, introducing the MegaScale system, and completed GPT-3 training in just 1.75 days. The MegaScale achieved a computing power utilization rate of 55.2%, surpassing NVIDIA's Megatron-LM. System optimizations included algorithm enhancements, communication overlap, operator optimization, data pipelining, and network performance tuning. Byte has established a GPU cluster with over 10,000 cards and is currently building a large-scale Hopper architecture cluster.

Taotian Group Collaborates with Aicheng Technology to Open Source the Megatron-LLaMA Large Model Training Framework

The Megatron-LLaMA framework, jointly developed by Taotian Group and Aicheng Technology, aims to enhance the training performance of large language models while reducing training costs and maintaining compatibility with the LLaMA community, achieving a 176% speedup in training on 32 GPUs.

TaoTian Group Collaborates with AiCheng Technology to Open Source the Large Model Training Framework Megatron-LLaMA

TaoTian Group, in collaboration with AiCheng Technology, has open sourced the large model training framework Megatron-LLaMA, improving the training performance of large language models and reducing training costs. Megatron-LLaMA achieves a 176% acceleration on 32-card training and exhibits linear scalability for large-scale training. Megatron-LLaMA has been open sourced on GitHub, and we will continuously monitor community development, promoting adaptive configuration and support for more models. The framework improves the gradient aggregation mechanism and optimizes the backpropagation flow.