Mobile AI Assistant on Llama: On-Device and Server Integration

Integrating Llama into a mobile app comes down to two questions: run the model locally or through a server? The choice dictates the entire architecture—from binary size to support costs. We've been through this path with dozens of projects, from medical chat agents to financial advisors. Below are the technical details that will save you months of experimentation.

How to Choose Between On-Device and Server Llama?

On-device — the model lives in the phone's memory, inference works without internet. Feasible for Llama 3.2 1B and 3B in INT4 quantization. Llama 3.2 3B INT4 occupies ~2 GB RAM and on an iPhone 15 Pro delivers 15–25 tokens/sec. This is the option for apps with strict privacy requirements (medical data never leaves the device) or offline use.

Server Llama — the model runs on your GPU server (or rented), the mobile client communicates via API. Allows you to use Llama 3.3 70B or Llama 3.1 405B—full-sized models indistinguishable in quality from GPT-4. Most commercial projects choose the server approach: easier to update the model, no RAM size limitation.

Why Is On-Device Still Relevant?

For scenarios requiring critical privacy (banking transactions, medical recommendations) or unstable internet, a local model is the only option. Moreover, you avoid server infrastructure and API request costs. Savings on server expenses with local deployment can be substantial. However, response quality is lower due to limited model size. For instance, in a medical consultation app for clinicians, we deployed Llama 3.2 3B Q4_K_M on iPad. Inference ran at 12 tokens/sec via Core ML, ensuring patient data never left the device and eliminating server costs. The client saw a 4x reduction in monthly infrastructure expenses compared to their cloud NLP pipeline.

// iOS — minimal wrapper over llama.cpp
class LlamaContext {
    private var context: OpaquePointer?

    init(modelPath: String) {
        var params = llama_context_default_params()
        params.n_ctx = 4096
        params.n_threads = 4  // fewer threads — less heat
        let model = llama_load_model_from_file(modelPath, llama_model_default_params())
        context = llama_new_context_with_model(model, params)
    }

    func generate(prompt: String, maxTokens: Int = 256) -> AsyncStream<String> {
        // tokenize → sample loop → detokenize
    }
}

Server Llama: Ollama and vLLM

For server deployment — Ollama (simplicity) or vLLM (performance). Ollama provides an OpenAI-compatible API: POST /api/chat, request format identical to OpenAI Chat Completions. A mobile client written for OpenAI works with Ollama unchanged — just change the base URL.

vLLM is preferable for production under load: continuous batching, tensor parallelism on multiple GPUs, throughput several times higher than Ollama.

Fine-Tuning: When and How

Fine-tuning is necessary when base Llama fails at specialized tasks: medical terminology, legal style, industry specifics. LoRA/QLoRA is the standard approach for fine-tuning on a single GPU. Trained adapters (~50–100 MB) are loaded on top of the base model. Deploying on-device reduces operational costs for cloud computing.

What's Included in the Work

1. Requirements analysis and architecture selection: on-device or server, model and quantization choice.
2. Runtime integration: building llama.cpp/ExecuTorch for iOS/Android, API wrapper.
3. Server setup: deploying Ollama/vLLM, load balancing, monitoring.
4. Testing: latency metrics, memory consumption, response quality.
5. Documentation and training: how to update the model, change parameters.
6. Post-launch support: integration warranty, consultation on modifications.

Implementation Process: Step by Step

1. Audit requirements: use cases, target devices, budget.
2. Choose model and runtime.
3. Integration and build.
4. Deploy server part (if needed).
5. Test metrics.
6. Documentation and handover.
7. Warranty support.

Time Estimates

Server Llama with Ollama and mobile client — 1–2 weeks. On-device via llama.cpp with builds for iOS/Android — 3–5 weeks. Fine-tuning + deployment — estimated separately. Get a consultation for your project. Leave a request—we will analyze the task and propose the optimal solution.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.