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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks
TL;DR
BALANCE introduces a hybrid inference framework combining autoregressive and speculative decoding for large language models in edge networks, significantly enhancing task throughput. This approach effectively addresses latency and memory challenges, outperforming traditional methods.
✦ Why It Matters
Implement BALANCE to optimize LLM inference in your edge computing applications today.
Key Takeaways
- Explore hybrid inference frameworks for edge computing.
- Consider user-specific needs when assigning decoding methods.
- Utilize polynomial-time algorithms for resource allocation.
- Test BALANCE against existing LLM inference methods.
How It Works
BALANCE operates by hosting both a small language model (SLM) and a large language model (LLM) on an edge server. It assigns users to either autoregressive decoding (AD) or speculative decoding (SD) based on their latency requirements.
The server runs both modes concurrently, allowing for efficient resource allocation. A polynomial-time algorithm breaks down the complex task throughput maximization problem into manageable sub-problems, ensuring optimal scheduling and resource distribution.