Dan Fu
PulseAugur coverage of Dan Fu — every cluster mentioning Dan Fu across labs, papers, and developer communities, ranked by signal.
-
Together AI to host open-source 0x Alpha model
Together AI has announced that the 0x Alpha model will be available on their platform starting tomorrow. This model is designed for inference and is open-source. Further details regarding its API, pricing, and benchmark…
-
Cursor partners with Together AI for real-time coding inference
Cursor has partnered with Together AI to enhance its AI-powered coding capabilities. Together AI has developed the necessary infrastructure to provide real-time inference, enabling Cursor's in-editor agents to generate …
-
Together AI details latency optimization with NVIDIA Blackwell
Together AI has detailed its approach to optimizing inference latency, highlighting the integration of various NVIDIA technologies with their own platform. Their system, Together ATLAS, leverages NVIDIA Blackwell, CUDA,…
-
MiniMax M3 launches with 1M-token context and MSA architecture
MiniMax has released its M3 model, featuring a novel Sparse Attention (MSA) architecture that enables a 1 million token context window and native multimodality. This new architecture significantly reduces computational …
-
Together AI: Inference benchmarks miss production realities
Inference benchmarks may not accurately reflect real-world production workloads, according to Dan Fu, VP of Kernels at Together. This is particularly true when running numerous concurrent coding agents that require larg…
-
Together AI kernels team optimizes GPUs with FlashAttention
The Together AI kernels team, including researchers Dan Fu and Tri Dao, developed FlashAttention, a software layer that significantly optimizes GPU performance for AI models. This breakthrough, achieved by applying data…
-
Together AI VP: AI not hitting hardware wall, efficiency gains untapped
Together AI's VP of Kernels, Dan Fu, argues that the pursuit of AGI is not hitting a hardware wall. He posits that current AI systems are significantly underutilizing existing hardware, with training runs often achievin…