AI inference and model deployment platforms encompass the hardware, runtimes and orchestration layers used to serve trained models in production — from hyperscale data‑center servers with custom accelerators to self‑hosted and edge inference stacks. As of 2026, pressure from rising cloud costs, latency/privacy requirements, and model diversity has driven a shift toward purpose‑built inference hardware (chiplets, SoCs, accelerator servers) and software stacks that prioritize throughput, energy efficiency, quantized runtimes and model composability. Key categories include decentralized AI infrastructure (on‑prem/edge servers, local model serving and orchestration) and AI data platforms that feed and monitor inference pipelines. Representative tools: Rebellions.ai provides energy‑efficient GPU‑class accelerators and a matching software stack for high‑throughput LLM and multimodal inference in hyperscale environments; Stable Code supplies compact, instruction‑tuned code models (≈3B parameters) designed for fast, private completion and on‑device deployment; Tabby is an open‑source, self‑hosted coding assistant combining IDE integrations with model serving and local‑first/cloud deployment options. Practical deployment trends include using smaller, task‑tuned models to reduce compute, hardware–software co‑design for power and throughput gains, quantization and sparsity for memory efficiency, and self‑hosted stacks to meet privacy and offline requirements. For operators, the focus is on matching model family and serving runtime to workload (latency vs throughput), validating energy and cost metrics, and adopting modular inference stacks that can run across accelerator servers, on‑prem clusters and edge devices.