Distributed LLM Execution

EdgeMob extends beyond single-device inference by enabling distributed execution of large language models (LLMs) across multiple mobile nodes. This approach allows models that exceed the capacity of an individual device to be segmented and executed collaboratively across a network of smartphones.

Layer-Splitting Architecture

• Large LLMs are divided into layer-level segments or compute blocks.

• Each participating mobile node is assigned one or more layers, executing its part of the model pipeline.

• Intermediate outputs are passed between devices over secure communication channels until the final inference result is produced.

Benefits

• Scalability: Even resource-constrained devices can contribute to running very large models by handling smaller workloads.

• Efficiency: Reduces the need for high-end GPUs by distributing workloads across widely available mobile CPUs and NPUs.

• Cost Reduction: Eliminates the financial overhead of centralized GPU clusters, leveraging existing hardware already owned by users.

SLA (Service Level Agreement) Improvements

• In early phases, distributed inference introduces latency and reliability trade-offs compared to centralized compute.

• Over time, three factors improve SLAs:

  1. Advancing Mobile Hardware: Each new generation of smartphones brings faster processors and more memory.

  2. Optimized Scheduling: Smarter orchestration reduces overhead in distributing workloads across nodes.

  3. Network Scaling: A larger pool of participating devices enables redundancy and parallelization.

• The long-term vision is to achieve near real-time inference for large models across a decentralized mobile network.

Practical Applications

• Running LLaMA-70B or similar large-scale models without centralized GPUs.

• Privacy-preserving distributed compute for sensitive workloads.

• Collective AI services where communities pool mobile devices to achieve inference capacity comparable to enterprise GPU clusters.

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Through distributed LLM execution, EdgeMob transforms individual devices into building blocks of a global, collaborative AI supercomputer, enabling large-scale inference that improves as the network and hardware ecosystem mature.