30x Faster Inference with Custom LLM SDK – Bringing GenAI to the Edge

A Deep Dive

1. Overview

This project was a pioneering internal R&D initiative focused on enabling fast, offline, and privacy-preserving LLM inference directly on handheld devices for one of the global technology leaders in mobile computing. 

The core objective was to develop a lightweight SDK that would allow application developers to easily integrate GenAI-powered features—such as natural language querying of product manuals—without relying on cloud infrastructure.

The initiative had four key goals:

1. Prove the feasibility of running meaningful GenAI use cases directly on edge devices, without compromising performance or usability.
2. Achieve fast and efficient on-device inference, including support for Retrieval-Augmented Generation (RAG) to ensure relevant and accurate responses from local data sources.
3. Design a developer-friendly API that abstracts away the complexity of working with LLMs, allowing non-ML specialists to build intelligent applications quickly and effectively.
4. Maintain a high quality of output that is competitive with cloud-based LLMs, while using significantly less compute and memory by optimizing for on-device resource constraints.

Key Outcomes

• Built a highly optimized SDK for edge LLM applications, leveraging Qualcomm’s GenAI stack.
• Achieved up to 30x faster inference compared to baseline edge implementations.
• Developed an offline RAG pipeline that matches the quality of cloud-based alternatives.
• Enabled real-time, internet-free GenAI features on consumer-grade devices.
  

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2. Client

A global mobile computing company exploring how to push the boundaries of GenAI deployment on their devices. 

• Industry: Manufacturing and Technology
• Market Position: A company with 10,000 employees.
• Achievements & Context:
  • Long-standing expertise in ML/AI at scale
  • Recognized for applying frontier AI tech in production settings
  • Internal experimentation aimed at driving client innovation forward

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3. Challenge

Business Challenge

Edge-based LLM solutions were unproven and immature, yet critical for enabling cost-efficient, low-latency, and offline AI capabilities in consumer devices. The team needed to validate whether a usable, high-quality GenAI solution could be built without relying on cloud infrastructure.

Technology Challenge

• Early-stage and fragmented ecosystem for on-device LLMs.
• Small models performed poorly; larger models exceeded typical device constraints.
• Lack of reliable retrieval mechanisms using edge-compatible embedding models.
• Manual-heavy workflow due to missing annotated datasets and evaluation tools.
• Existing solutions had unacceptable latency or limited customization.
  

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4. Solution

We approached the challenge as a full-stack GenAI engineering problem, optimizing both model performance and the developer experience.

Model & Inference Optimization

• Benchmarked open-source frameworks (MLC, llama.cpp)
• Transitioned to Qualcomm’s libGenie + QNN, enabling hardware-accelerated inference
• Built an optimized C++ inference engine with Java/Kotlin bindings
• Reduced prefill stage latency by up to 30x on mid-range Android devices

Retrieval Pipeline Innovation

• Evaluated and selected on-device embedding models
• Developed custom document indexing (by chunks, summaries, and LLM-generated Q&A pairs)
• Achieved higher query–document alignment, boosting retrieval precision

Evaluation & Quality Monitoring

• Created evaluation sets using OpenAI and Gemini APIs
• Applied LLM-as-a-judge scoring to simulate end-user relevance
• Manually verified outputs for critical use cases

Data Processing & SDK Abstraction

• PDF digitization via Docling to convert manuals to markdown
• Designed a lightweight, application-friendly SDK interface
• Ensured internal and external teams could adopt it without LLM expertise

Tech Stack

• Languages: C++, Python, Java, Kotlin
• Frameworks & Tools: libGenie, QNN, Faiss, Docling
• LLMs: Meta LLaMA (1B–8B variants)
• Cloud APIs for Eval: OpenAI / Gemini

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5. Process

Key Development Stages

1. Feasibility & Benchmarking:
   Compared inference speed and quality across open-source and commercial edge AI runtimes.
2. SDK Prototyping & Optimization:
   Engineered a modular SDK to abstract model handling, indexing, and RAG orchestration.
3. Data & Retrieval Tuning:
   Introduced advanced chunking and summary-indexing techniques to improve answer relevance.
4. Evaluation Pipeline:
   Automated dataset generation, model response scoring, and quality monitoring with human-in-the-loop reviews.

Team Involved

• GenAI Engineer (Optimization & SDK architecture)
• AI Engineer (Retrieval & Evaluation)
• Full-Stack Developer (Android integration)
• Data Engineer (Data pipelines & format conversion)

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6. Outcome

Quantitative Results

• 30x speedup in LLM prefill latency on edge devices
• Enabled real-time on-device inference for GenAI assistants
• Significant retrieval quality improvement through hybrid chunking + summary indexing
• Built an SDK enabling integration in hours, not weeks

Qualitative Results

• Full offline mode improves UX in low-connectivity environments
• On-device processing enhances user data privacy
• Enables new GenAI features in embedded apps without cloud dependence

Lessons Learned

• On-device AI is viable – with careful model selection and hardware-specific optimization
• Evaluation is essential – automated + manual loops helped maintain quality
• SDK abstraction is key to democratizing GenAI use by non-ML developers

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7. Summary

Final Thoughts

This initiative validated that generative AI can run efficiently on edge devices, delivering cloud-level performance while improving speed, cost, and privacy. Our custom SDK serves as a foundation for embedding LLM-powered features into mobile and embedded apps, unlocking new product possibilities.