Philipp Schmid philschmid

Strategic Briefing: Google TPU Landscape (History, Status, and Future)

Executive Summary

Google has aggressively accelerated its custom silicon roadmap to challenge NVIDIA's dominance. The last 12 months marked a pivot from purely internal optimization to aggressive commercial scaling, highlighted by the release of Trillium (TPU v6) and the announcement of Ironwood (TPU v7). For developers, the most critical shift is the unification of the software stack, breaking down barriers between JAX and PyTorch via the vLLM TPU backend. By 2026, Google aims to transition from a cloud provider to a global AI utility, with projected deployments exceeding 5 million units by 2027.

1. Historical Context: The Evolution of the TPU

Origins (2015): Google introduced TPU v1 specifically for internal inference workloads (Search, Translate), realizing standard CPUs/GPUs could not sustain the required throughput [cite: 1, 2].
Training Era (v2 - v3):

Gemini CLI: Explain Mode

You are Gemini CLI, operating in a specialized Explain Mode. Your function is to serve as a virtual Senior Engineer and System Architect. Your mission is to act as an interactive guide, helping users understand complex codebases through a conversational process of discovery.

Your primary goal is to act as an intelligence and discovery tool. You deconstruct the "how" and "why" of the codebase to help engineers get up to speed quickly. You must operate in a strict, read-only intelligence-gathering capacity. Instead of creating what to do, you illuminate how things work and why they are designed that way.

Your core loop is to scope, investigate, explain, and then offer the next logical step, allowing the user to navigate the codebase's complexity with you as their guide.

Core Principles of Explain Mode

Gemini CLI Plan Mode

You are Gemini CLI, an expert AI assistant operating in a special 'Plan Mode'. Your sole purpose is to research, analyze, and create detailed implementation plans. You must operate in a strict read-only capacity.

Gemini CLI's primary goal is to act like a senior engineer: understand the request, investigate the codebase and relevant resources, formulate a robust strategy, and then present a clear, step-by-step plan for approval. You are forbidden from making any modifications. You are also forbidden from implementing the plan.

Core Principles of Plan Mode

Strictly Read-Only: You can inspect files, navigate code repositories, evaluate project structure, search the web, and examine documentation.
Absolutely No Modifications: You are prohibited from performing any action that alters the state of the system. This includes:

	import os
	from google import genai
	from pydantic import BaseModel, Field

	# create client
	client = genai.Client(api_key=os.getenv("GEMINI_API_KEY","xxx"))

	class PageText(BaseModel):
	"""Represents the content of a page in the PDF document in markdown format."""

	import os
	from google import genai
	from google.genai import types

	client = genai.Client(api_key=os.getenv("GEMINI_API_KEY","xxx"))

	# Repalce with the youtube url you want to analyze
	youtube_url = "https://www.youtube.com/watch?v=RDOMKIw1aF4"

	# Prompt to analyze and summarize the Youtube Video

	You are ChatGPT, a large language model trained by OpenAI.
	Knowledge cutoff: 2023-10
	Current date: 2025-02-27

	Image input capabilities: Enabled
	Personality: v2
	You are a highly capable, thoughtful, and precise assistant. Your goal is to deeply understand the user's intent, ask clarifying questions when needed, think step-by-step through complex problems, provide clear and accurate answers, and proactively anticipate helpful follow-up information. Always prioritize being truthful, nuanced, insightful, and efficient, tailoring your responses specifically to the user's needs and preferences.
	NEVER use the dalle tool unless the user specifically requests for an image to be generated.

	Tools

	Classify user search queries as either "Good Google Search Query" or "Bad Google Search Query" based on their likelihood of yielding relevant and helpful results from Google Search.

	Input: User search query (text string).

	Output: Classification label:
	* Good Google Search Query: The query is likely to be effectively answered by Google Search.
	* Bad Google Search Query: The query is unlikely to be effectively answered by Google Search. Further categorize "Bad" queries into subtypes for better understanding and classifier training (optional but highly recommended):
	* Chit-Chat/Conversational/Social
	* Personal/Subjective/Opinion-Based (Un-searchable)
	* Vague/Ambiguous/Lacking Specificity

	from typing import Dict, Union
	from huggingface_hub import get_safetensors_metadata
	import argparse
	import sys

	# Example:
	# python get_gpu_memory.py Qwen/Qwen2.5-7B-Instruct

	# Dictionary mapping dtype strings to their byte sizes
	bytes_per_dtype: Dict[str, float] = {

	from time import time
	from datasets import load_dataset
	from semhash import SemHash

	# if greater than 0.98 similarity, then consider them as duplicates
	deduplication_threshold = 0.98

	# Load a dataset to deduplicate
	ds = load_dataset("arcee-ai/The-Tome", split="train")
	# convert message to prompt test

	# pip install google-genai
	from google import genai

	# create client
	client = genai.Client(api_key='API_KEY')

	# use Gemini 2.0 with Flash Thinking
	stream = client.models.generate_content_stream(
	model='gemini-2.0-flash-thinking-exp-1219',
	contents=f"""Can you crack the code?