AI Chip Architectures

Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical Blog

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing…

Where AI Startups Scale to Production

Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.

TheStage AI – Faster, Cheaper AI Inference

Accelerate models on NVIDIA & edge. Full guides for setup, optimization & deploy. ANNA, QLIP, Elastic Models, CLI & API. Built for AI teams & devs.

We're launching two specialized TPUs for the agentic era.

The eighth generation of Google’s TPU includes two specialized chips that will power the future of AI.

Automated Architecture Synthesis via Targeted Evolution | Liquid AI

Today, we report advances in automated neural network architecture design and customization. We developed algorithms for the synthesis of tailored architectures (STAR), based on evolutionary algorithms applied to a numerical representation for model architectures derived from a new design theory. STAR automates the process of architecture discovery and optimization, turning it into an end-to-end process. With these methods, we have been able to tailor architectures to custom tasks, metrics, and hardware. We used STAR to synthesize hundreds of different designs that outperform strong Transformer and hybrid architectures in quality, with smaller caches and number of parameters.

"AI" centralization

So it seems like NVIDIA has agreed to buy Hugging Face. NVIDIA is the company building most of the chips used in “AI” data centers and Hugging face runs probably the biggest repository for data sets and open weight “AI” models in the world (Hugging Face also offers inference but not at relevant rates and […]

Understanding TPUs vs GPUs in AI: A Comprehensive Guide

Explore the differences between Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) in AI.

mudler/LocalAI

LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.

Nvidia invests $5 billion into Intel to jointly develop PC and data center chips

Intel will help build x86 chips with Nvidia RTX GPU chiplets

Open-Source Agentic Inference Benchmark | InferenceX

Compare AgentX, InferenceX's long-context, multi-turn coding scenario, with fixed-sequence AI inference across chips and frameworks. Public NVIDIA and AMD runs update when configurations change.

How Nvidia’s Jensen Huang became AI’s global salesman

Chipmaker’s chief is urging countries to build their own AI ecosystems — but using its tech

pguso/ai-agents-from-scratch

Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.

The Universal Execution Layer for AI

Optimize any AI model on any engine, across all hardware. Dria’s topology-aware compiler and peer-to-peer runtime merge CPUs, GPUs, NPUs & chiplets into one fabric—maximising utilisation, cutting inference cost and ending vendor lock-in.

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

Differential acceleration of cyber, math, and AI

Which AI brands have the most satisfied users?

NVIDIA Shatters MoE AI Performance Records With a Massive 10x Leap on GB200 'Blackwell' NVL72 Servers, Fueled by Co-Design Breakthroughs

Scaling performance on MoE AI models is one of the industry constraints, but it appears that NVIDIA has managed to make a breakthrough.