TPU vs Nvidia: Training vs Inference and Business Model Differences

1. TPU vs Nvidia: Training vs Inference

A. Training Big Models

Nvidia Strengths

  • CUDA ecosystem: dominant ML software stack.
  • Hardware + networking: NVLink, InfiniBand, multi-GPU scaling.
  • Default choice for new model training due to maturity and compatibility.

Google TPU Strengths

  • Highly optimized tensor cores and pod-scale systems.
  • Excellent cost/performance on some LLM workloads.
  • JAX/XLA enables highly efficient execution if designed for it.

Limitations

  • TPU ecosystem is not as mature as CUDA.
  • More friction porting research pipelines designed for Nvidia.

Training Summary:
Nvidia leads for general-purpose training. TPUs excel when fully embraced within Google’s ecosystem.

B. Inference (Serving Models)

Why inference is different

  • Billions of small calls vs a few huge training runs.
  • Cost per token and energy efficiency dominate.

Nvidia Inference

  • Same GPU can train & serve models.
  • Strong inference frameworks like TensorRT and Triton.

TPU Inference

  • New generations (e.g., Ironwood) optimized for low-cost, high-throughput serving.
  • Excellent performance-per-dollar for very large scale inference.

Inference Summary:
Nvidia is a solid general-purpose choice; TPUs may be dramatically cheaper at hyperscale.

2. Business Model: Sell Chips vs Sell Cloud & Services

Nvidia’s Model

  • Sells GPUs, systems, networking.
  • Massive margins.
  • CUDA is the moat.
  • Broad customer base.

Google’s Model

  • Doesn’t sell TPUs; sells TPU compute via Google Cloud.
  • TPUs also power internal products:
    • Search, Ads, YouTube, Gmail/Docs AI, Gemini APIs.
  • TPU investment yields:
    • Cloud revenue,
    • Better product performance,
    • Internal cost savings.

Strategic Differences

  • Nvidia: wants everyone dependent on CUDA.
  • Google: wants workloads on Google Cloud, using TPUs and Gemini.

TL;DR

  • Training: Nvidia remains dominant; TPUs strong when aligned with Google’s stack.
  • Inference: TPUs engineered for large-scale efficiency; may beat Nvidia on $/token.
  • Business Models: Nvidia sells hardware; Google sells cloud and uses TPUs to enhance its products.