OpenAI has struck a deal to use Google’s cloud computing infrastructure for AI despite the two companies’ fierce competition in […]
Category: AI infrastructure
Meta Introduces KernelLLM: An 8B LLM that Translates PyTorch Modules into Efficient Triton GPU Kernels
Meta has introduced KernelLLM, an 8-billion-parameter language model fine-tuned from Llama 3.1 Instruct, aimed at automating the translation of PyTorch […]
This AI paper from DeepSeek-AI Explores How DeepSeek-V3 Delivers High-Performance Language Modeling by Minimizing Hardware Overhead and Maximizing Computational Efficiency
The growth in developing and deploying large language models (LLMs) is closely tied to architectural innovations, large-scale datasets, and hardware […]
Huawei Introduces Pangu Ultra MoE: A 718B-Parameter Sparse Language Model Trained Efficiently on Ascend NPUs Using Simulation-Driven Architecture and System-Level Optimization
Sparse large language models (LLMs) based on the Mixture of Experts (MoE) framework have gained traction for their ability to […]
Google backs Elementl Power to build advanced nuclear sites across America
Google is continuing its support of nuclear energy. Following an agreement with Kairos Power last year, the search giant just […]
Serverless MCP Brings AI-Assisted Debugging to AWS Workflows Within Modern IDEs
Serverless computing has significantly streamlined how developers build and deploy applications on cloud platforms like AWS. However, debugging and managing […]
Allen Institute for AI (Ai2) Launches OLMoTrace: Real-Time Tracing of LLM Outputs Back to Training Data
Understanding the Limits of Language Model Transparency As large language models (LLMs) become central to a growing number of applications—ranging […]
LLMs No Longer Require Powerful Servers: Researchers from MIT, KAUST, ISTA, and Yandex Introduce a New AI Approach to Rapidly Compress Large Language Models without a Significant Loss of Quality
HIGGS — the innovative method for compressing large language models was developed in collaboration with teams at Yandex Research, MIT, […]
Google AI Introduces Ironwood: A Google TPU Purpose-Built for the Age of Inference
At the 2025 Google Cloud Next event, Google introduced Ironwood, its latest generation of Tensor Processing Units (TPUs), designed specifically […]
This AI Paper Introduces a Machine Learning Framework to Estimate the Inference Budget for Self-Consistency and GenRMs (Generative Reward Models)
Large Language Models (LLMs) have demonstrated significant advancements in reasoning capabilities across diverse domains, including mathematics and science. However, improving […]