RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
  1. >
  2. Home
  3. /Hardware
  4. /Google Tensor G4
UNIT · GOOGLE · MOBILE-SOC
12 GB UNIFIEDmobile·Reviewed May 2026

Google Tensor G4

Pixel 9 SoC. Google's mobile chip optimized for Gemini Nano + on-device transcription / summarization. NPU TOPS not publicly disclosed by Google; treat as on-par with mid-Snapdragon based on Gemini Nano benchmarks.

Released 2024
▼ CHECK CURRENT PRICE· 1 retailer

Google Tensor G4

Check on Amazon→

Affiliate disclosure: as an Amazon Associate and partner of other retailers, we earn from qualifying purchases. The verdict on this page is our editorial opinion; affiliate links never influence what we recommend.

RUNLOCALAI SCORE
See full leaderboard →
106/ 1000
DD-tier
Estimated
Throughput
14/ 500
VRAM-fit
0/ 200
Ecosystem
60/ 200
Efficiency
77/ 100

Extrapolated from 60 GB/s bandwidth — 4.8 tok/s estimated. No measured benchmarks yet.

WORKLOAD FIT
Try other hardware →

Plain-English: Doesn't fit modern chat models usefully.

7B chat△
Marginal
14B chat△
Marginal
32B chat✗
Doesn't fit
70B chat✗
Doesn't fit
Coding agent△
Marginal
Vision (≤8B VLM)△
Marginal
Long context (32K)✗
Doesn't fit
✓Comfortable — fits with headroom
~Tight — works, no slack
△Marginal — needs aggressive quant
✗Doesn't fit usefully

Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.

BLK · VERDICT

Our verdict

OP · Fredoline Eruo|VERIFIED MAY 8, 2026
4.8/10

What it does well

The Google Tensor G4 is Google's custom Pixel phone SoC — co-designed with Samsung based on Exynos architecture and tuned for Google's first-party AI features (Gemini Nano on-device, Pixel Recorder transcription, Magic Editor, Best Take). 8 CPU cores + Mali-G715 GPU + Tensor Processing Unit (TPU) + 12 GB unified memory in Pixel 9 Pro. The chip ships in Pixel 9 / 9 Pro / 9 Pro XL at $799-$1,099 retail. Tensor G4's Google-tuned TPU is the canonical Android-side AI accelerator for first-party Google AI features — Gemini Nano runs natively, Pixel-specific features (Add Me, Pixel Studio) ship tuned to the silicon.

Where it breaks

  • Raw silicon performance is below Snapdragon 8 Elite / 8 Gen 3. Tensor G4's CPU + GPU lag the contemporary Qualcomm flagships in benchmarks. Google prioritizes AI-feature integration over peak compute.
  • Same iOS-equivalent sandbox limitations on Android. No proper LLM development workflow on the phone.
  • TPU framework support is essentially Google-first-party. Third-party LLM frameworks targeting Tensor are thinner than Snapdragon's Qualcomm AI Hub ecosystem.
  • Memory + bandwidth caps at phone tier. Sub-3B class on-device only.
  • End-of-feature-support window. Google supports Pixel for 7 years; Tensor G4 is well-positioned for long-horizon support — Google's strongest pitch.

Ideal model range

  • Sweet spot: Google's first-party Pixel AI features (Gemini Nano, Magic Editor, Best Take, Pixel Studio).
  • Sweet spot: Pixel-form factor + AI as integrated feature, not the reason.
  • Sweet spot: Long-horizon Android phone support (Google's 7-year update commitment).
  • Bad fit: Anything beyond Google's first-party AI features.

Verdict

Buy Pixel 9 / 9 Pro / 9 Pro XL for the Pixel use case (camera, Google ecosystem, first-party AI features). Tensor G4 is the chip that makes Pixel-specific AI features work elegantly. For most readers, this verdict is informational reference about the silicon powering Pixel's AI integration.

Skip this if you want maximum raw phone performance (Snapdragon 8 Elite wins on benchmarks), you want Apple Intelligence (A18 Pro on iPhone 16 Pro), or you're shopping for AI development hardware (wrong tier).

How it compares

  • vs Snapdragon 8 Elite → 8 Elite has higher raw CPU + GPU performance + 45 TOPS NPU. Tensor G4 has tighter Google-first-party AI integration. Pick by ecosystem priority.
  • vs Snapdragon 8 Gen 3 → Generation match. Pick by phone OEM (Samsung/OnePlus vs Pixel).
  • vs Apple A18 Pro → Different ecosystems entirely. Pick by Android vs iOS preference.
BLK · OVERVIEW

Overview

Pixel 9 SoC. Google's mobile chip optimized for Gemini Nano + on-device transcription / summarization. NPU TOPS not publicly disclosed by Google; treat as on-par with mid-Snapdragon based on Gemini Nano benchmarks.

Retailers we'd check:Amazon

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

Featured in this stack

The L3 execution stacks that pick this hardware as a recommended component, with the one-line note explaining the role it plays in each.

  • Stack · L3·Homelab tier·Role: Pixel-only path
    Android on-device AI stack — Phi-3.5 Mini / Llama 3.2 3B via MLC LLM or Qualcomm AI Hub

    Tensor G4 ships in Pixel 9. Google's Gemini Nano runs natively. NPU TOPS aren't publicly disclosed — community benchmarks suggest mid-Snapdragon parity. Tensor's path is Pixel-locked.

BLK · SPECS

Specs

VRAM0 GB
System RAM (typical)12 GB
Power draw5 W
Released2024
Backends

Frequently asked

Does Google Tensor G4 support CUDA?

Google Tensor G4 does not support CUDA. Use Vulkan-compatible tools (llama.cpp Vulkan backend) or check vendor-specific runtimes.

Where next?

Buyer guides
  • Best GPU for local AI →
  • Best laptop for local AI →
  • Best Mac for local AI →
  • Best used GPU for local AI →
Troubleshooting
  • CUDA out of memory →
  • Ollama running slowly →
  • ROCm not detected →
  • Model keeps crashing →

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.

RUNLOCALAI

Operator-grade instrument for local-AI hardware intelligence. Hand-written verdicts. Real benchmarks. Reproducible commands.

OP·Fredoline Eruo
DIR
  • Models
  • Hardware
  • Tools
  • Benchmarks
  • Will it run?
GUIDES
  • Best GPU
  • Best laptop
  • Best Mac
  • Best used GPU
  • Best budget GPU
  • Best GPU for Ollama
  • Best GPU for SD
  • AI PC build $2K
  • CUDA vs ROCm
  • 16 vs 24 GB
  • Compare hardware
  • Custom compare
REF
  • Systems
  • Ecosystem maps
  • Pillar guides
  • Methodology
  • Glossary
  • Errors KB
  • Troubleshooting
  • Resources
  • Public API
EDITOR
  • About
  • About the author
  • Changelog
  • Latest
  • Updates
  • Submit benchmark
  • Send feedback
  • Trust
  • Editorial policy
  • How we make money
  • Contact
LEGAL
  • Privacy
  • Terms
  • Sitemap
MAIL · MONTHLY DIGEST
Get monthly local AI changes
Monthly recap. No spam.
DISCLOSURE

Some links on this site are affiliate links (Amazon Associates and other first-class retailers). When you buy through them, we earn a small commission at no extra cost to you. Affiliate links do not influence our verdicts — there are cards we rate highly that we don't have affiliate relationships with, and cards that sell well that we refuse to recommend. Read more →

SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Compare alternatives

Hardware worth comparing

Same VRAM tier and the one step above and below — so you can frame the buying decision against real options.

Same VRAM tier
Cards in the same memory band
  • Qualcomm Snapdragon 8 Elite
    qualcomm · 0 GB VRAM
    5.3/10
  • Apple A18 Pro
    apple · 0 GB VRAM
    5.0/10
  • Qualcomm Snapdragon 8 Gen 3
    qualcomm · 0 GB VRAM
    4.5/10
  • Apple M4 (iPad Pro)
    apple · 0 GB VRAM
    5.0/10
  • Apple A17 Pro
    apple · 0 GB VRAM
    4.7/10
  • Apple M3 Ultra
    apple · 0 GB VRAM
    10.0/10
Step up
More VRAM — bigger models, more context
  • Apple M3 Ultra
    apple · 0 GB VRAM
    10.0/10
  • Apple M2 Ultra
    apple · 0 GB VRAM
    9.9/10
  • Apple M4 Ultra
    apple · 0 GB VRAM
    10.0/10
Step down
Less VRAM — cheaper, more constrained
  • AMD Ryzen AI 9 HX 370 (Strix Point)
    amd · 0 GB VRAM
    3.9/10
  • Intel Core Ultra 7 258V (Lunar Lake)
    intel · 0 GB VRAM
    3.8/10
  • NVIDIA GeForce RTX 4060
    nvidia · 8 GB VRAM
    5.3/10