Alphabet
Alphabet Acquires Intersect for $4.75B to Expand AI Compute
Alphabet announces $4.75 billion acquisition of data center builder Intersect, dramatically expanding compute infrastructure for cloud services and AI workloads.
Alphabet
Alphabet announces $4.75 billion acquisition of data center builder Intersect, dramatically expanding compute infrastructure for cloud services and AI workloads.
OpenAI
OpenAI is reportedly in advanced talks with Amazon for a $10 billion investment that could include a strategic chip partnership, potentially reshaping AI infrastructure and compute access.
AI infrastructure
AI infrastructure startup Runware secures $50M to build a universal API connecting developers to multiple generative AI models, streamlining access to image, video, and audio synthesis capabilities.
Nvidia
Nvidia purchases SchedMD, maker of Slurm open-source workload manager used by most AI supercomputers. The acquisition strengthens Nvidia's grip on AI training infrastructure.
Intel
Intel is reportedly nearing a deal to acquire SambaNova Systems, an AI chip startup that could strengthen Intel's position in the competitive AI accelerator market against Nvidia.
LLM deployment
A technical deep-dive into deploying quantized large language models using AWQ compression, vLLM inference engine, and FastAPI for production-ready AI applications.
LLM compression
Learn how to reduce a 7 billion parameter language model from ~14GB to 4.5GB using quantization, pruning, and knowledge distillation while maintaining accuracy.
AI infrastructure
The Model Context Protocol (MCP) is reshaping how AI tools integrate with external systems. Here's how ChatGPT, GitHub Copilot, and Cursor are implementing this new standard for AI agent connectivity.
Nvidia
NVIDIA's GB200 NVL72 GPU system accelerates Mistral 3 model inference by 10x, leveraging advanced tensor parallelism and NVLink architecture. The optimization demonstrates significant improvements in AI model deployment efficiency.
LLM
Deep dive into the three core parallelization strategies for large language model inference: data parallel, model parallel, and pipeline parallel approaches. Essential techniques for scaling AI systems efficiently.
AI Models
Compact language models are challenging LLM dominance through knowledge distillation, quantization, and efficient architectures. Technical advances enable production deployment at fraction of computational cost while maintaining performance.
LLM Training
Microsoft's DeepSpeed optimization library transforms large language model training through ZeRO memory optimization, 3D parallelism, and infrastructure innovations that make training trillion-parameter models feasible on consumer hardware.