Why I Switched from Chrome to Puma on My Pixel: A Hands-On Review and Privacy Setup

Hook: Why I swapped Chrome for Puma on my Pixel (and why students should care)

Too many web-development learners juggle fragmented tools, browser tabs, and cloud-based AI that leaks your data. If you want a Chrome alternative that keeps AI on-device, respects privacy, and gives you classroom-ready features, here's a hands-on review from switching my Pixel to Puma browser in late 2025 — and how to set it up, test its local AI, tune privacy vs performance, and configure it for students.

TL;DR — The most important takeaways (inverted pyramid)

• Puma is a local AI browser with on-device LLM support that works on Android and iPhone and can run without cloud-send.
• On a modern Pixel (9/9a/series), Puma delivers better privacy controls than Chrome by design; performance is comparable for everyday browsing.
• For students: Puma's local AI shines for summarizing lecture notes, drafting citations, creating flashcards, and offline study tasks — all without pushing prompts to third-party servers.
• This article gives step-by-step setup for Android/iPhone, how to test local AI features, privacy checks you can run, and student-specific presets.

The context in 2026: why local AI browsers matter now

By 2026, the push for on-device AI accelerated. Late 2024–2025 saw major OS vendors add APIs for running LLMs locally, and developers shipped smaller, optimized models for mobile CPUs and NPUs. Regulators and institutions also increased scrutiny on cloud-transmitted user prompts, so a browser that keeps AI local is no longer niche — it's a practical privacy upgrade for students and educators.

That’s the space Puma occupies: a mobile browser that treats the LLM as a first-class, local component. Below I document my real-world setup, experiments, and customizations on a Pixel phone so you can reproduce the workflow.

Before you start: what you’ll need

• A modern Pixel phone (Pixel 8/9/9a or later recommended) or iPhone running iOS 16+/17+.
• Puma browser app (Play Store or App Store) — latest version as of late 2025/early 2026.
• Wi‑Fi and 2–4 GB free storage for model downloads (depends on model size you choose).
• Optional: a laptop for Lighthouse/WebPageTest if you want performance benchmarking.

Step-by-step: Installing Puma on Android (Pixel) and iPhone

Android (Pixel) — quick setup

1. Open Google Play Store and search for Puma browser. Install the official app (confirm developer name matches Puma).
2. Launch Puma and accept the minimum permissions. Don’t grant mic/camera until you need them; local AI can work without those.
3. Go to Settings • Local AI (or similar). The browser will prompt you to download a model. Choose a model size:
   • Small/fast: under ~100MB — good for summaries and flashcards.
   • Medium: ~200–800MB — better for nuanced prompts.
   • Large: 1GB+ — best offline accuracy if your Pixel has the storage.
4. Download the model over Wi‑Fi. Puma will unpack and calibrate — this can take a few minutes. If you want to understand cloud vs local tradeoffs for models and platform delivery, see a cloud platform review.
5. Return to Settings and set your default search engine, privacy profile, and enable on-device only for AI if you want no cloud fallbacks.

iPhone — the differences to note

1. Install from the App Store. iOS sandboxing means Puma cannot substitute the system WebView without user action; functionality is the same but model file sizes and storage may be constrained.
2. Puma will request permission to store files locally. Approve the storage access so models can be cached in the app container.
3. Download a model and set on-device only. iOS users should watch storage usage in Settings • General • iPhone Storage. For guidance on device lifecycles and refurbished hardware considerations, see refurbished phones & home hubs.

First-run checklist (what I changed immediately)

• Set a custom home page for campus tools and saved lectures.
• Enable Strict Tracker Blocking and disable third-party cookies.
• Limit microphone/camera until needed.
• Turn on Reader Mode and PDF annotation if you annotate readings for class. Offline-first and resilient document workflows are explored in guides on making diagrams resilient.
• Enable session-saving or workspace tabs so you can restore research sessions.

Testing local AI: four practical experiments

These are reproducible tests any student or teacher can run to judge Puma's on-device AI capability.

1) Summarize a news article into 5 bullet points

1. Open an article in Puma.
2. Tap the local AI icon and paste: "Summarize this article into five study-ready bullet points and include one citation format (APA)."
3. Compare: Run the same prompt in Chrome using a cloud LLM (if available) and note whether Puma kept the prompt local (see network monitors below).

Expected result: Puma returns a concise, citation-ready summary. The latency is typically lower because the model runs locally; the trade-off is depth vs model size.

2) Create flashcards from lecture notes

1. Paste or import lecture notes into Puma’s note field.
2. Prompt: "Generate 10 Anki-style flashcards from the below notes, each with Q/A and a one-line source tag."
3. Export the results as CSV or copy into AnkiMobile/AnkiDroid.

Result: Fast generation, offline export. This is a game-changer for low-connectivity study sessions.

3) Verify that prompts are local (privacy test)

1. Use an app like GlassWire or Android’s Data Usage for a quick glance at Puma’s network traffic.
2. Run the same complex prompt twice: once with Puma set to on-device only, once with cloud fallback enabled. Watch outgoing connections and endpoint domains.
3. Optionally, tether your Pixel to a laptop and run Wireshark or HTTP Toolkit to inspect traffic (advanced). For instrumentation and monitoring practices helpful in audits, see modern observability.

Expected: With on-device enabled, Puma should show minimal or no AI-related outbound network connections. If you see traffic to third-party AI endpoints, toggle settings and retry.

4) Measure perceived performance vs Chrome

1. Pick a set of sites you use in class (news, journal PDF, educational app).
2. Run a page-load stopwatch comparison: open the page in Puma and then in Chrome; repeat 5 times and take the median times. For deeper metrics, run Lighthouse in remote device mode on your laptop and consult latency playbooks like latency playbook.
3. Check battery drain by using each browser for a 30-minute study session and noting battery % change.

In my tests on a Pixel 9a, general browsing speed was comparable. The local AI responses were faster than cloud LLM calls because they avoid network latency. Battery impact depends on model size — larger models will use more CPU/NPU cycles and reduce battery life faster.

Privacy comparison: Puma vs Chrome

Privacy is not binary; it's a set of trade-offs. Here’s how Puma and Chrome compare across common concerns:

• AI prompt handling: Puma: can run on-device, limiting cloud exposure. Chrome: typically routes through Google services when using built-in AI (unless configured otherwise).
• Tracking & cookies: Puma: strong tracker blocking by default and easy toggles. Chrome: has improved but still ties deeply into Google services and ecosystem telemetry unless aggressively tuned.
• Permissions model: Both follow platform rules. Puma encourages minimal permissions for AI; Chrome may request permissions for integration with other Google apps. For designing permission models and developer-side secrets/PKI implications around OS-level AI, see developer experience and PKI trends.

Practical rule: If you want to keep student prompts, drafts, and research queries private (no cloud logs), prefer on-device-first browsers like Puma.

Performance trade-offs and tuning

On-device LLMs are advancing fast, but they still trade off accuracy/nuance for latency and privacy. Here are tuning tips:

• Model size: Choose the smallest model that meets your needs. For summarizing and flashcards, medium models often hit the sweet spot.
• CPU vs NPU: Pixel devices with an NPU will run models faster and more efficiently. Enable NPU acceleration in Puma if available.
• Background usage: Prevent large models from updating or running heavy tasks on battery saver by restricting background activity for Puma in system settings.
• Sync frequency: If you use cloud sync for bookmarks or history, tune sync frequency to conserve battery and avoid unnecessary cloud calls.

Student presets: making Puma classroom-ready

Students need focused browsing, citation help, and offline study tools. Here are presets and a simple workflow you can copy.

Preset: "Study Mode"

• Enable bold Reader Mode on pages with long text.
• Turn on on-device AI and set AI temperature lower (0–0.3) for factual output.
• Enable session workspaces and pin tabs for course LMS, calendar, and a notes page.
• Enable PDF annotation and quick export to Google Drive (optional) or local file. Offline-first document workflows are discussed in guides like making diagrams resilient.

Homework workflow (10 minutes per reading)

1. Open the reading in Puma and enter Reader Mode.
2. Ask local AI for a 200-word summary and five keywords.
3. Prompt AI to generate three short-answer questions and one multiple-choice question with answers (use for self-quizzing).
4. Export flashcards as CSV and import into your preferred SRS app (see how micro-app tooling enables simple exports: micro-app tooling).

Advanced checks: verifying privacy and behavior

If you teach or care about audits, these checks help verify Puma’s local behavior.

1. Confirm in-app settings that on-device AI is selected and cloud fallback is off.
2. Check Android/iOS app permissions and revoke unnecessary ones (Contacts, SMS, etc.).
3. Monitor network traffic with GlassWire or HTTP Toolkit during an AI prompt to verify no outbound AI domains are contacted. Instrumentation guidance for such audits is covered in modern observability.
4. Review Puma’s privacy policy and changelog for recent updates; community audits and transparency reports are a plus.

Limitations I ran into (be honest with students)

• Large-model quality may not match the biggest cloud LLMs for complex reasoning tasks.
• Model downloads can be large — manage storage if your Pixel is tight on space.
• Some web integrations (deep Google services) behave differently than Chrome due to platform sandboxing.

2026 trends and short-term predictions

Looking ahead, expect the following to shape mobile browsers and on-device AI:

• Model compression advances: More powerful models under 500MB will become common by 2026–27, reducing trade-offs.
• OS-level LLM APIs: Android and iOS will expand APIs to let browsers and apps share secure on-device models without duplicate downloads; this ties into developer experience and PKI trends for secure model access: developer experience & PKI trends.
• Hybrid privacy modes: Browsers will offer automatic routing: sensitive prompts stay local; non-sensitive tasks use cloud acceleration for complexity. See approaches to privacy-first personalization for product decisions: privacy-first personalization.
• Education tooling: Expect deeper integrations for LMS and plagiarism-aware local assistants that help students produce original summaries and study aids.

Actionable checklist: switch from Chrome to Puma (10-step)

1. Install Puma from official app store.
2. Download a medium-sized local LLM over Wi‑Fi.
3. Enable On-Device AI and Strict Tracker Blocking.
4. Set Reader Mode and PDF annotation as defaults.
5. Create a "Study Mode" workspace and pin course tabs.
6. Run a privacy check with GlassWire during a test prompt.
7. Export a summary/flashcard set to your SRS app.
8. Tweak model size if battery or storage is an issue.
9. Compare page load times via stopwatch or Lighthouse remote runs.
10. Document and share your preset with classmates or students.

Final verdict — who should switch?

If your priorities are privacy, offline AI utility, and student-focused features, Puma is a solid Chrome alternative on Pixel and iPhone. It isn’t a replacement for Chrome’s deep Google integrations, but for learners and teachers who need quick, private summarization, study workflows, and local AI tools, it’s an excellent option.

Closing: practical takeaways

• Local AI browsers like Puma are now practical on modern phones — they lower privacy risk and speed up common study tasks.
• Trade-offs: choose model size to balance battery, storage, and output quality.
• For students: use the Study Mode workflow to turn readings into flashcards and test questions in minutes.

Call to action

Try Puma on your Pixel this week: follow the 10-step checklist above, run the four AI experiments, and bookmark your study preset. Found a useful setting or want a ready-made Study Mode config to share with your class? Download my free preset and step-by-step PDF at webbclass.com/puma-preset — and drop a comment with your test results so we can build a community-tested student workflow.

Related Reading

• Designing Privacy-First Personalization with On-Device Models — 2026 Playbook
• Refurbished Phones & Home Hubs: A Practical Guide for 2026 — Buying, Privacy, and Integration
• Future-Proofing: On-Device AI & Offline-First UX — Playbook
• Zero Trust for Generative Agents: Designing Permissions and Data Flows
• Best Portable Power Stations 2026: Jackery vs EcoFlow vs Budget Alternatives
• Mac mini M4 Deals Explained: Which Configuration Is the Best Value Right Now?
• How to Monitor PR Spikes from Big Campaigns (Like Disney’s Oscar Push) in Search and Backlinks
• Which US Host Cities Are Best for Visitors Without Match Tickets — 48‑Hour Alternatives
• Curating an Eid Playlist: Reworking Pop Hits into Family-Friendly Celebrations