AI Hardware
DABench-LLM: New Framework Benchmarks Post-Moore AI Accelerators
Researchers introduce DABench-LLM, a standardized framework for evaluating dataflow AI accelerators designed for large language model inference in the post-Moore era.
AI Infrastructure
multimodal AI
MMR-Bench: New Benchmark Tests AI Model Routing for Multimodal Ta
Researchers introduce MMR-Bench, a comprehensive benchmark evaluating how well routing systems direct queries to optimal multimodal LLMs across diverse visual reasoning tasks.
Microsoft
Microsoft Unveils Maia 200 AI Chip to Challenge Amazon and Google
Microsoft announces its Maia 200 custom AI accelerator, entering direct competition with Amazon and Google in the race to build proprietary silicon for AI workloads.
multi-agent systems
AgentScope: Building Scalable Multi-Agent LLM Systems
AgentScope provides a flexible framework for orchestrating multiple LLM agents with built-in communication protocols, fault tolerance, and scalability features for complex AI workflows.
Big Tech
Big Tech Q2 Earnings to Test Market Faith in AI Spending
Upcoming earnings from Microsoft, Google, Meta, and Amazon will reveal whether massive AI infrastructure investments are delivering returns—a pivotal moment for the entire AI ecosystem.
LLM
KV Cache Explained: The Hidden Engine Powering Fast LLM Inference
Understanding Key-Value caching in transformer architectures reveals how modern LLMs achieve fast token generation. This core optimization technique is essential for efficient AI inference.
Nvidia
Nvidia Invests in AI Inference Startup Baseten's New Round
Nvidia reportedly backs Baseten in new funding round, signaling chipmaker's strategic push into AI inference infrastructure that powers real-time video generation and synthetic media applications.
AI Hardware
Micron: AI Memory Chip Shortage to Extend Past 2026
Micron declares AI-driven memory shortage 'unprecedented,' predicting supply constraints will persist beyond 2026 as demand for high-bandwidth memory outpaces production capacity.
Microsoft
Microsoft's Anthropic AI Spending Nears $500M Mark
Microsoft's spending on Anthropic AI is reportedly on track to reach $500 million, signaling a major strategic shift in AI partnerships beyond its OpenAI investment.
LLM compression
Hierarchical Sparse Plus Low Rank: A New Approach to LLM Compress
New research introduces hierarchical sparse plus low rank compression for LLMs, combining structured sparsity with matrix decomposition for efficient model deployment.
LLM
Universal Latent Space Enables Zero-Shot LLM Routing
New research introduces a universal latent space approach for cost-efficient LLM routing, enabling zero-shot model selection without task-specific training data or expensive benchmarking.
LLM Quantization
FLRQ: Faster LLM Quantization via Low-Rank Matrix Sketching
New quantization method FLRQ achieves up to 2.5x faster compression of large language models while maintaining accuracy through flexible low-rank matrix approximation techniques.