This comparison examines photonic (optical) AI chips versus NVIDIA Blackwell GPU instances for both inference and training, focusing on energy efficiency, throughput, software ecosystem, and deployment fit across hyperscale data centers, edge vision platforms, and decentralized AI infrastructure. Photonic processors promise substantially lower energy per operation and very high aggregate bandwidth, making them attractive for large-scale, latency-sensitive inference. However, as of 2025 their software stacks, numerical programmability, and support for large-scale distributed training remain more limited compared with GPU ecosystems. NVIDIA Blackwell instances continue to be the practical default for model training and broadly supported inference due to mature tooling (CUDA, cuDNN, TensorRT), ecosystem integrations, and cloud availability. Industry consolidation around GPU-first toolchains is evident — for example, the deci.ai domain now reflects NVIDIA-branded content following a May 2024 acquisition — which reinforces NVIDIA’s software and services reach. At the same time, alternatives targeting energy-efficient inference are emerging: Rebellions.ai develops purpose-built chiplets, SoCs, servers and a GPU-class software stack aimed at high-throughput, energy-efficient LLM and multimodal inference in hyperscale environments. Parallel trends include interest in decentralized, open-source infrastructure (e.g., Tensorplex Labs) that couples model development with blockchain/DeFi primitives for marketplace and edge use cases; these approaches favor flexible, heterogeneous hardware and could accelerate non‑GPU deployments at the edge. Choosing between photonic hardware and Blackwell GPUs depends on workload (inference vs training), maturity needs (software and scaling), power constraints, and deployment model (cloud, on-prem hyperscale, or decentralized/edge). Expect hybrid deployments and continued software convergence to determine adoption over the next 12–24 months.