Accelerating the search for antimicrobial peptides with Google Antigravity
deepmind.google·2h ago
AI Research
arXiv CS.AI·3d ago·5 min read
Adaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy
TL;DR
Adaptive DHiSparse introduces a novel approach to speculative decoding using a load-adaptive key-value (KV) hierarchy, significantly enhancing throughput and concurrency. It achieves up to 4 times the concurrency and 2.3 to 3.7 times the output compared to existing methods.
✦ Why It Matters
Implement Adaptive DHiSparse to enhance your model's throughput and concurrency in long-context applications today.
Key Takeaways
- Explore Adaptive DHiSparse for improved long-context serving.
- Utilize speculative decoding to enhance throughput.
- Reduce memory fetches during low load conditions.
- Test with GLM-5.3 to evaluate performance gains.
How It Works
Adaptive DHiSparse employs a hierarchical sparse key-value (KV) structure that dynamically adjusts to the load. It supports speculative decoding, which allows the system to predict and fetch only the necessary entries, thus optimizing memory usage.
By retaining additional historical data when GPU memory is available, it enhances the efficiency of data retrieval. The system also implements exact fused retrieval to ensure that the required working set is protected while maximizing throughput.