#Gemma

Articles about Gemma — exploring patterns, best practices, and real-world implementations in production systems.

4 posts tagged with gemma. ← All posts

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Pratik Dhanave · ·6 min read

Gemma on Flutter

This is where the concepts become code. With a quantized Gemma model and the flutter_gemma plugin, running a real LLM entirely on the user's phone is a handful of Dart calls — load the model, open a chat session, stream tokens into your UI. The plugin hides the runtime; you write an app.

This is where the concepts become code. With a quantized Gemma model and the flutter_gemma plugin, running a real LLM entirely on the user's phone is a handful of Dart calls — load the model, open a chat session, stream tokens into your UI.

#On-Device AI#Flutter#Gemma#Edge AI#LLMs
Pratik Dhanave · ·6 min read

The On-Device Runtime

Between your quantized model file and a running feature sits the runtime — the engine that loads the model, maps it onto the phone's CPU, GPU, or NPU, and turns tokens into text. You rarely write inference math yourself; you pick a runtime and let it handle the brutal complexity of executing a neural network across thousands of different devices.

Between your quantized model file and a running feature sits the runtime — the engine that loads the model, maps it onto the phone's CPU, GPU, or NPU, and turns tokens into text across thousands of different devices.

#On-Device AI#Edge AI#Flutter#Gemma
Pratik Dhanave · ·6 min read

Making Models Fit: Quantization for the Edge

On the cloud, quantization is an optimization you reach for to cut costs. On a phone, it's the difference between the model running and not running at all. Nearly every on-device LLM you'll ever ship is quantized, because full-precision weights simply don't fit — so understanding the bit-width trade-off is non-negotiable for edge AI.

On the cloud, quantization cuts costs. On a phone, it's the difference between the model running and not running at all — nearly every on-device LLM you'll ship is quantized, so the bit-width trade-off is non-negotiable for edge AI.

#On-Device AI#Edge AI#Gemma#Performance#Flutter#LLMs
Pratik Dhanave · ·6 min read

Why On-Device AI?

For a decade the default answer to "where does the AI run?" was "someone else's GPU, over the network." On-device AI flips that: the model runs on the phone in the user's hand, and once you internalize what that changes — privacy, offline capability, latency, and cost all at once — a whole class of apps becomes possible that a cloud API can't build.

On-device AI flips the default — the model runs on the phone in the user's hand. Once you internalize what that changes — privacy, offline capability, latency, and cost all at once — a whole class of apps becomes possible that a cloud API can't build.

#On-Device AI#Edge AI#Flutter#Gemma#APIs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.