AI inference accelerators and runtimes cover the hardware, low‑level software, and hosted services used to run large language models (LLMs) and multimodal systems in production. This topic spans high‑throughput datacenter silicon (NVIDIA Blackwell and alternatives like Groq), purpose‑built inference chiplets/SoCs and servers, edge vision accelerators, and the managed cloud inference services and runtimes that tie them together. Through 2025 the market has sharpened around three priorities: energy efficiency at hyperscale, low‑latency edge deployments for vision and robotics, and flexible software stacks that support heterogeneous hardware. Rebellions.ai exemplifies the energy‑first approach with purpose‑built accelerators and a GPU‑class software stack aimed at high‑throughput, energy‑efficient LLM and multimodal inference in hyperscale data centers. Groq and NVIDIA Blackwell remain focal points for providers and customers weighing throughput, latency and software ecosystem tradeoffs. At the same time, managed cloud inference services continue to abstract hardware complexity and provide production‑grade runtimes for scaling models. Decentralized models of compute and governance are emerging alongside centralized offerings. Tensorplex Labs represents an open‑source, blockchain‑integrated approach that couples model development with DeFi primitives (staking, cross‑network incentives), pointing to new architectures for distributed inference capacity. Industry consolidation is also visible — a site audit of deci.ai shows the domain now serving NVIDIA‑branded content following a May 2024 acquisition — underscoring how software, tooling and services are consolidating around major silicon vendors. For engineers and platform teams, the current landscape requires evaluating energy and latency targets, runtime compatibility, and deployment model (cloud, edge, decentralized) to match workloads and cost constraints.