AI and Deep Learning Accelerators Beyond GPUs in 2025 (www.bestgpusforai.com)

The article surveys the 2025 AI-acceleration landscape and argues that ASICs, FPGAs and NPUs are maturing into complements — not replacements — for GPUs. It documents how specialized silicon targets the key pain points of modern AI: energy efficiency, inference latency, and workload-specific throughput. Concrete examples anchor the analysis: Google’s TPU lineage (Trillium/Trillium v6 cited at ~4.7× chip-level gains), AWS Trainium2 (advertised at 83.2 petaflops for massive training servers), Cerebras WSE‑3 (wafer-scale design with ~900k cores and ~4 trillion transistors), and claims that Intel/Habana’s Gaudi3 can outpace NVIDIA H100 on some long-output LLM inference tasks. The piece also covers Graphcore, Groq, SambaNova, and edge-focused NPUs, and explains where FPGAs’ reconfigurability and ASICs’ efficiency fit into production pipelines. For practitioners the takeaway is practical: choose hardware by workload and deployment constraints. ASICs win for high-volume, low-power inference and edge devices once NRE is amortized; FPGAs offer a middle ground for prototyping and evolving models; GPUs retain dominance for flexible training and rapid research iteration. Cloud instances from GCP/AWS/Azure lower the barrier to experiment. Overall, the ecosystem is trending heterogeneous — selection depends on scale, latency/power budgets, and cost trade‑offs, not on a single “winner.”