This topic examines how hardware constraints and deployment choices shape LLM design, performance and operational trade‑offs — contrasting on‑device/edge models with cloud‑hosted systems. On‑device and local‑first approaches (enabled by tools like Tabby and Cline) prioritize low latency, data locality, and offline operation through model compression, quantization and NPU/GPU‑aware runtimes. Cloud‑hosted and enterprise platforms (exemplified by Harvey and large provider stacks) favor larger models, centralized data management, and easier lifecycle governance, with platforms such as Qodo addressing code/test governance across distributed SDLCs. Relevance in late 2025 stems from two converging trends: wider availability of mobile and embedded NPUs that make multi‑bit quantized LLMs practical on edge devices, and continued consolidation and hardware optimization in the cloud (notably the NVIDIA alignment after Deci.ai’s 2024 acquisition), which pushes specialized compiler/runtime toolchains for high‑throughput inference. Decentralized infrastructure projects (e.g., Tensorplex Labs) are introducing alternative deployment topologies that combine staking and resource marketplaces, adding new considerations for trust, latency and cost predictability. Key evaluation dimensions include latency, throughput, energy per token, memory footprint, model accuracy under pruning/quantization, privacy/regulatory requirements, and operational complexity (orchestration, updates, observability). Practical comparisons require hardware‑aware benchmarks (Tensor cores, NPUs, mobile accelerators), optimized runtimes (TensorRT, ONNX/MLIR toolchains), and governance controls for multi‑tenant or decentralized environments. In short, the right deployment depends on workload characteristics (real‑time vs batch), domain constraints (privacy, compliance), and the available hardware/software stack — a decision increasingly shaped by edge‑first tooling, cloud GPU economies, and emerging decentralized platforms.