The AutoML landscape has shifted. While some pioneers have faded into “zombie” states, powerhouses like AutoGluon and FLAML are redefining the field in 2025. Discover which open-source tools survived the hype and which are officially deprecated.
Autograd was the indie darling of AI research—elegant, academic, and purely Pythonic. But as the world shifted to GPUs, its beauty became a bottleneck. Learn why Google performed an autopsy on Autograd to engineer the future: JAX.
Hollywood’s gatekeepers are vanishing. With the “Great GPU Cascade” making compute power a commodity, 2028 marks the era of the personal blockbuster. Simply input a script, and AI generates a feature-length masterpiece, complete with consistent, photorealistic characters.
As millions of high-end GPUs cross the 18-month depreciation threshold, a massive supply of “paid-off” compute is hitting the market. This shift will collapse rental prices, making world-class AI infrastructure affordable for startups and SMBs everywhere.
By 2030, China’s “Manhattan Project” is predicted to deliver domestic EUV lithography and a unified software stack. This sovereign infrastructure aims to render Nvidia’s CUDA moat irrelevant, creating a parallel AI superpower ready for massive global export.
TorchTPU marks a seismic shift in AI infrastructure, bridging the gap between PyTorch’s flexibility and Google’s specialized silicon. By neutralizing Nvidia’s software dominance, this initiative offers developers a high-performance “escape hatch” to scale massive models more efficiently.
NVIDIA’s acquisition of SchedMD marks a turning point for HPC. While Slurm remains open-source, the community is weighing its future. We dive into the history of Slurm and the viable alternatives for a vendor-neutral AI infrastructure.
Google is positioned to exploit the growing power gap in AI infrastructure. As Nvidia struggles with high power density requirements, Google can flood the market with efficient TPUs that deploy in existing data centers and reshape AI chip economics.
ZLUDA 5 is emerging as the most credible open source challenge to CUDA by enabling unmodified CUDA applications to run on non NVIDIA GPUs with near native performance. It reshapes the competitive landscape across AI inference, HPC, and GPU virtualization.
Explore how open-source embedding models like BGE, E5-Large, and MiniLM are reshaping retrieval and RAG systems. Learn how they integrate with pgvector and vector databases to power enterprise-grade search, analytics, and AI infrastructure.
Reinforcement pre-training applies reinforcement learning principles early in model development to guide reasoning, exploration, and safety behaviors. By shaping learning before alignment, it bridges the gap between pre-training and RLHF—enhancing intelligence formation in large AI systems.
CoreWeave has rapidly become the leader in GPU-as-a-Service and AI factory infrastructure. But as it moves into software and developer tools through acquisitions like Weights & Biases and OpenPipe, the real test will be sustaining innovation beyond compute.
Predibase’s Turbo LoRA and LoRAX are redefining efficient fine-tuning and multi-adapter serving for large language models. By combining speculative decoding with shared GPU infrastructure, they aim to make AI customization faster, cheaper, and production-ready at scale.
OpenPipe’s Agent Reinforcement Training (ART) empowers LLM agents to learn multi-step tasks via reinforcement learning. CoreWeave’s acquisition signals a shift toward developer-ready, open-source agentic AI infrastructure, offering startups a turnkey path from experimentation to scalable agent training.
Machine learning accelerators are redefining AI infrastructure in 2025. From GPUs and TPUs to NPUs and photonic chips, the focus has shifted from raw power to smarter compute orchestration—balancing performance, memory, and efficiency across heterogeneous hardware systems.
