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Gemini 3.0: The Triumph of Vertical Integration

Created in November 29, 2025

2025   ·   ai-infra   tpu   google   dist-sys   gemini   ·   systems

The release of Gemini 3.0 isn’t just a milestone for model quality; it’s a victory lap for Google’s custom infrastructure stack. While the rest of the industry scrambles for GPUs, Google has quietly demonstrated that owning the entire vertical—from the silicon (TPU) to the compiler (XLA) to the framework (JAX) is the ultimate competitive advantage. Read more about it on Google Cloud’s blog, “Introducing Cloud TPU v5p and AI Hypercomputer”

The Silicon: TPUv6 and OCS

At the heart of Gemini 3.0 is the TPUv6. Unlike general‑purpose GPUs, TPUs are domain‑specific architectures (DSAs) designed for one thing: massive matrix multiplication. Google’s official TPU architecture page details the MXU and HBM design

The real magic, however, isn’t just the compute; it’s the networking. Gemini 3.0 was likely trained on pods connected via Optical Circuit Switches (OCS). This allows for dynamic topology reconfiguration. If a rack fails, the network can physically re‑route light paths in milliseconds, bypassing the fault without stalling the training run. This level of resilience is unheard of in standard InfiniBand clusters.

The Software: JAX and XLA

Hardware is useless without a compiler. XLA (Accelerated Linear Algebra) is the secret weapon here. It fuses operations to minimize memory bandwidth usage—the bottleneck of modern LLMs. The JAX documentation’s “Thinking in JAX” guide explains XLA optimizations

When you write code in JAX, you aren’t just executing kernels; you are describing a computation graph that XLA optimizes globally. For Gemini 3.0, this meant:

Here is a simplified example of what this looks like in practice:

import jax
import jax.numpy as jnp
from jax.sharding import Mesh, PartitionSpec, NamedSharding

# 1. Define the physical mesh of TPUs (e.g., 4x4 grid)
mesh = Mesh(jax.devices(), ('x', 'y'))

# 2. Define how data should be partitioned
# 'x' dimension of data maps to 'x' dimension of mesh
# 'y' dimension of data maps to 'y' dimension of mesh
sharding = NamedSharding(mesh, PartitionSpec('x', 'y'))

# 3. Write "single device" code, let the compiler shard it
@jax.jit(out_shardings=sharding)
def matmul(a, b):
    return jnp.dot(a, b)

# The compiler automatically handles communication across the 50,000 chips

Why This Matters

For systems engineers, Gemini 3.0 is a manifesto for vertical integration. It demonstrates that the next frontier in AI scaling isn’t just about bigger models, but about the tight coupling of silicon, network, and compiler.

In a post-Moore’s Law era, you can’t just buy performance; you have to architect it. Google’s ability to co-design the TPU for the OCS and XLA for the TPU is what allows them to push the boundaries of what’s physically possible.