This topic examines modern LLM inference and fine‑tuning frameworks — contrasted across QVAC Fabric, NVIDIA Triton, Ray, and Hugging Face — and how Model Context Protocol (MCP) servers integrate observability, data access and secure tooling into production pipelines. It’s about choosing the right mix of low‑latency inference, distributed orchestration and fine‑tuning workflows for 2025 workloads: large open and proprietary models, quantized and sparsified weights, parameter‑efficient fine‑tuning (LoRA/PEFT), and tight cost/latency constraints. Relevance: as LLMs scale, teams must balance throughput, latency, hardware heterogeneity and compliance. Triton remains focused on GPU‑optimized, low‑latency serving and multi‑backend model hosting; Ray provides distributed compute and orchestration for sharded inference, batch jobs and distributed fine‑tuning; Hugging Face consolidates model hub, training utilities, PEFT toolchains and managed endpoints; QVAC Fabric positions itself as an inference fabric for cross‑accelerator routing, high‑throughput LLM serving and workload placement. Key tradeoffs include latency vs throughput, vendor lock‑in, and integration complexity for mixed CPU/GPU/accelerator fleets. MCP servers (Arize Phoenix, pydantic’s mcp-run-python, AWS and Azure MCP portfolios, Supabase, n8n) supply standardized, secure APIs for tracing, evaluation, data access and workflow automation — enabling observability, sandboxed code execution, and unified access to cloud services and databases across inference and tuning pipelines. Together these layers address operational needs: experiment tracking, secure tool access, model evaluation, and end‑to‑end orchestration. Evaluations should consider model format support, quantization and sharding features, orchestration primitives, and how MCP integrations support observability and governance in production.