Quick Answer
The Google Tensor G4 is the successor to the Tensor G2, offering a more advanced manufacturing process and architectural improvements for better overall efficiency and AI performance. While both chips share a focus on on-device machine learning, the G4 typically provides a generational boost in CPU and GPU tasks, alongside newer AI models and features.
Google Tensor G4 vs Google Tensor G2: A Detailed Comparison
When evaluating the progression of Google’s in-house mobile processors, comparing the Tensor G4 to its predecessor, the Tensor G2, provides insight into the company’s design priorities. This comparison is important for understanding the evolution of performance, efficiency, and specialized AI capabilities between two generations. This article will break down the key differences in manufacturing, CPU/GPU design, AI and machine learning features, and overall capabilities to help clarify what each chipset offers.
Manufacturing Process and Architecture
The fundamental difference between these two systems-on-a-chip (SoC) lies in their construction. This shift impacts power efficiency and potential performance.
- Google Tensor G2: Manufactured using Samsung’s 5nm process technology. It features a tri-cluster CPU with two ARM Cortex-X1 cores, two Cortex-A78 cores, and four Cortex-A55 cores.
- Google Tensor G4: Built on Samsung’s more advanced 4nm process (4LPP+). It utilizes an updated CPU configuration, generally moving to newer ARM cores like the Cortex-X3, A715, and A510 for improved performance-per-watt.
The move to a 4nm process typically allows the G4 to perform the same tasks using less power or achieve higher performance levels within a similar thermal envelope compared to the G2.
CPU and GPU Performance
Raw processing and graphics power see a generational uplift with the Tensor G4, though both chips are designed with a balanced approach rather than chasing peak benchmark scores.
- CPU: The Tensor G4’s newer CPU cores provide a noticeable improvement in both single-threaded and multi-threaded tasks. This translates to smoother app launches, better multitasking, and improved responsiveness in daily use.
- GPU: The G4 integrates an upgraded ARM Mali-G715 GPU. This offers better graphics rendering for gaming and visual effects compared to the Mali-G710 MP7 found in the Tensor G2, supporting more advanced graphics APIs and features.
In synthetic benchmarks, the Tensor G4 generally scores higher than the G2. However, the real-world difference is often most apparent in sustained performance during intensive tasks, where the G4’s efficiency gains help maintain speeds for longer.
AI, Machine Learning, and Features
Both chips are built around Google’s strength in artificial intelligence, but the G4 incorporates a newer generation of the Tensor Processing Unit (TPU) and supporting hardware.
- Tensor G2: Focused on enabling features like advanced computational photography (e.g., Magic Eraser, Photo Unblur), real-time language translation, and improved voice recognition on-device.
- Tensor G4: Enhances these capabilities with a faster, more efficient TPU. It can run larger and more complex AI models on the device, powering next-generation features like more sophisticated camera post-processing (e.g., Video Boost, Night Sight Video), generative AI models, and even more responsive assistant functionalities.
The core philosophy remains the same—offloading AI tasks to a dedicated, efficient processor—but the G4 executes them faster and can handle more ambitious applications.
Connectivity and Other Components
The integrated modems and supporting silicon also see updates between generations.
- Modem: The Tensor G4 typically integrates a newer, more power-efficient cellular modem (like the Exynos 5300) compared to the Exynos 5123 in the G2. This can contribute to better battery life during cellular use and improved connectivity.
- ISP (Image Signal Processor): The G4’s ISP is more advanced, supporting higher-resolution sensor data and enabling the chip to process complex computational photography pipelines for the cameras it’s paired with.
- Security: Both chips include a dedicated Titan M2 security core for hardware-level protection. The G4 may include additional security enhancements tied to its newer architecture.
Comparison Table: Tensor G4 vs Tensor G2
| Feature | Google Tensor G2 | Google Tensor G4 |
|---|---|---|
| Manufacturing Process | Samsung 5nm (5LPE) | Samsung 4nm (4LPP+) |
| CPU Architecture | 2x Cortex-X1, 2x Cortex-A78, 4x Cortex-A55 | 1x Cortex-X3, 4x Cortex-A715, 4x Cortex-A510 |
| GPU | ARM Mali-G710 MP7 | ARM Mali-G715 |
| AI Processor | Next-gen Tensor Processing Unit (TPU) | Next-gen Tensor Processing Unit (TPU) – newer generation |
| Modem | Exynos 5123 (Integrated) | Exynos 5300 (Integrated) |
| Key AI Features | Magic Eraser, Photo Unblur, Live Translate, Assistant Voice Typing | Enhanced versions of G2 features, plus Video Boost, Night Sight Video, more advanced on-device generative AI |
| Typical Device Launch | Pixel 7 series, Pixel 7a, Pixel Fold | Pixel 8 series, Pixel 8a |
Frequently Asked Questions (FAQ)
What is the main difference between the Tensor G4 and G2?
The main differences are the move to a more efficient 4nm manufacturing process, an updated CPU and GPU architecture for better performance, and a newer-generation Tensor Processing Unit (TPU) for enhanced on-device AI capabilities.
Is the Tensor G4 significantly more powerful than the G2?
It provides a clear generational improvement. Users can expect better CPU/GPU performance, improved power efficiency leading to potentially better battery life under load, and the ability to run more advanced AI features.
Do both chips support the same camera features?
While they share a foundation in computational photography, the Tensor G4’s more powerful ISP and TPU enable newer, more processing-intensive features like Video Boost and enhanced video stabilization that are not available on the G2.
Which devices use the Tensor G2 and G4?
The Tensor G2 was used in the Pixel 7 series, Pixel 7a, and Pixel Fold. The Tensor G4 is found in the Pixel 8 series and the Pixel 8a.
Is the AI performance better on the Tensor G4?
Yes, the Tensor G4 can run AI models faster and more efficiently. This allows for more complex on-device AI tasks, quicker processing of photo enhancements, and support for newer generative AI features.
Final Thoughts
The Google Tensor G4 represents a logical and measurable step forward from the Tensor G2. It refines the formula with a more modern manufacturing process, updated core designs, and enhanced dedicated AI hardware. The G2 remains a capable chip focused on delivering a strong AI-powered experience, but the G4 builds upon that foundation with gains in efficiency, raw performance, and the scope of possible on-device machine learning applications. The choice in practice is often tied to the device generation, with the G4 powering newer models that can leverage its full potential.