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.
Dive into the essential building blocks of modern AI: MLPs, Softmax, LayerNorm, and memory management. Discover how these core compute kernels shape neural network performance and why optimizing them for GPUs and accelerators matters.
Ray is an open-source Python framework that scales AI and ML workloads across CPUs, GPUs, and clusters. From hyperparameter tuning to real-time model serving, Ray simplifies distributed computing, making research and production pipelines faster and more efficient.
Feature stores and real-time pipelines are essential for production ML, ensuring consistent, low-latency features. Open-source tools like Feast, Hopsworks, and Feathr provide scalable, flexible, and observable pipelines, enabling teams to deploy robust, reliable machine learning at scale.
DSPy is an open-source framework that lets developers program large language models with structured, modular code instead of relying on prompts. It enables scalable, self-optimizing AI pipelines, offering reliability, flexibility, and faster iteration for complex AI workflows.
Deploying large-scale machine learning requires orchestrating feature engineering, model evaluation, and inference pipelines. While integrated platforms simplify this, open-source tools offer flexibility, transparency, and control, enabling teams to build robust, customizable AI inference workflows on their own.
