Enterprise GenAI inference servers and platforms are the runtime layer that turns large language and multimodal models into production services with predictable latency, throughput, cost and governance. By 2026, deployments increasingly combine vendor runtimes (NVIDIA Triton), cloud-optimized servers (Red Hat AI Inference Server for AWS Trainium/Inferentia), and managed low‑latency offerings (Hugging Face Infinity) to meet divergent enterprise SLAs and cost targets. Key trends: specialized inference silicon and software stacks (e.g., Rebellions.ai’s energy‑efficient accelerators) are driving lower operating cost and emissions for hyperscale deployments; model optimizations (quantization, compilation, multi-backend serving) and standardized runtimes (Triton, ONNX/ORT integrations) reduce friction for multi-cloud and hybrid setups; and the rise of multimodal and Retrieval-Augmented Generation (RAG) workloads increases demand for integrated data and vector stores (Activeloop Deep Lake) plus robust interaction-logging and fine-tuning pipelines (OpenPipe). Enterprise toolchains now span hardware, inference runtimes, data platforms and observability: LlamaIndex and MindStudio help build and orchestrate document agents and no/low-code agent workflows; OpenPipe and Deep Lake handle interaction capture and multimodal data; RagaAI provides testing, observability and guardrails for agentic systems; developer productivity tools (e.g., GitHub Copilot) accelerate integration and deployment. Together these components address real-world requirements—latency, throughput, energy efficiency, model governance, and continuous evaluation—while enabling safe scaling of GenAI services. This topic is timely because operational costs, regulatory scrutiny, and heterogeneity of accelerator hardware are reshaping inference architecture choices; enterprises must evaluate both server runtimes and surrounding data and observability platforms to deploy GenAI reliably at scale.