The Hardware That Makes AI Possible
CPUs, GPUs, TPUs, and NPUs The post The Hardware That Makes AI Possible appeared first on Towards Data Science.
InfoWorld AI·
We’re seeing an interesting infrastructure tug of war today where GPU clouds are being pulled in two directions. For the economics of AI to work, the enterprise market needs to carve expensive hardware into smaller, shareable units and hand it to customers on demand, similar to how CPUs are doled in public cloud infrastructure. But the more the providers push GPUs to behave like elastic cloud infrastructure, the more they run into the reality that this GPU hardware was never built for safe multitenant use, fast fault recovery, or clean isolation between workloads. That tension is becoming one of the defining operational problems of the AI infrastructure market. When a gamer launches Steam or the Epic Games Store on their laptop, they don’t have to worry about which GPU is being scheduled, how memory is going to be divided, or really any of the security boundaries or hardware assignment issues on their PC. For consumer PCs, these issues are not just hidden from view, they are irrelevant
Read full articleCPUs, GPUs, TPUs, and NPUs The post The Hardware That Makes AI Possible appeared first on Towards Data Science.
In this tutorial, we implement a hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for CUDA-style kernels in Python. We prepare a Colab-friendly environment and check GPU, driver, CUDA, and cuTile availability before running kernels. We then build tiled vector addition, matrix addition, and matrix multiplication, keeping a PyTorch fallback so the notebook stays executable. We validate correctness against PyTorch and benchmark median runtimes at every stage. The post NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab appeared first on MarkTechPost.
Xiaomi's MiMo team, with TileRT, released MiMo-V2.5-Pro-UltraSpeed, a serving mode for the MiMo-V2.5-Pro model. It decodes over 1000 tokens per second on a 1-trillion-parameter model using a single 8-GPU commodity node. The post Xiaomi MiMo and TileRT Push a 1-Trillion-Parameter Model Past 1000 Tokens Per Second on Commodity GPUs appeared first on MarkTechPost.
Model Context Protocol (MCP) has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds to devops tools, MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands. Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand. The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use ca
Xbox Showcase 2026 spotlighted AAA hits and new hardware while web3 titles stayed offstage. We unpack distribution, UX and economics holding blockchain games back.
Google released the Colab CLI, letting developers and AI agents run local code on remote Colab GPU and TPU runtime The post Google’s New Colab CLI Lets Developers and AI Agents Run Python on Remote Colab GPUs and TPUs From the Terminal appeared first on MarkTechPost.
SpaceX has secured a major compute agreement withGoogle ahead of its planned Nasdaq listing, adding another large customer to its expanding AI infrastructure business. A regulatory filing by SpaceX said Google will pay the company $920 million per month from…
I set up an AI agent on a rented GPU, pointed it at a training script, and went to bed. By morning it had run 40 experiments, improved validation loss by 5.9%, and cut memory usage from 44 GB to 17 GB. It also spent four hours chasing a bug that a linter introduced behind […]