https://www.cnbc.com/2025/11/21/nvidia-gpus-google-tpus-aws-trainium-comparing-the-top-ai-chips.html
Custom ASICs, or application-specific integrated circuits, are now being designed by all the major hyperscalers, from Google's TPU to Amazon's Trainium and OpenAI's plans with Broadcom. These chips are smaller, cheaper, accessible and could reduce these companies' reliance on Nvidia GPUs. Daniel Newman of the Futurum Group told CNBC that he sees custom ASICs "growing even faster than the GPU market over the next few years."
Besides GPUs and ASICs, there are also field-programmable gate arrays, which can be reconfigured with software after they're made for use in all sorts of applications, like signal processing, networking and AI. There's also an entire group of AI chips that power AI on devices rather than in the cloud. Qualcomm, Apple and others have championed those on-device AI chips.
- Strengths: Extremely efficient for large-scale training & inference of models like Gemini, using systolic arrays for massive matrix multiplication. Excellent cost-performance (e.g., 4x better for inference). Tightly integrated with Google's network for massive scaling.
- Weaknesses: Less flexible; designed for specific AI workloads, not general-purpose computing or HPC.
- Best For: Google's internal services (Search, YouTube), large model training, inference at massive scale.
- Strengths: Custom silicon (Trainium for training, Inferentia for inference) designed for performance/cost optimization in AWS, offering better efficiency than GPUs for many cloud workloads.
- Weaknesses: Like TPUs, less flexible than GPUs for novel research.
- Best For: AWS customers needing cost-effective, scalable AI compute within the AWS ecosystem.
- Strengths: Unmatched flexibility, broad software support (CUDA), runs on-prem/cloud/edge, ideal for R&D, diverse models, and staying at the research frontier. The standard for most AI breakthroughs.
- Weaknesses: Higher power consumption and cost for highly specific, large-scale tasks where ASICs excel.
- Best For: General AI development, novel model architectures, hybrid cloud/on-prem deployments, research.
