arXiv:2602.20217v2 Announce Type: replace Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore the dynamic computational overhead of attention in long-context scenarios. We propose KnapSpec, a training-free framework that reformulates draft model selection as a knapsack problem to maximize tokens-per-time throughput. By decoupling Attention and MLP layers and modeling their hardware-specific latencies as functions of context length, KnapSpec adaptively identifi
The continuous improvement in LLM decoding efficiency is a critical bottleneck for wider deployment and cost reduction, driving innovation in this specific area.
Improved LLM inference efficiency directly translates to lower operational costs, faster response times, and broader accessibility for advanced AI applications.
This new method moves beyond static heuristics, allowing for more adaptive and hardware-aware optimization of LLM inference, particularly in long-context scenarios.
- · LLM operators
- · Cloud AI providers
- · Developers using LLMs
- · AI hardware manufacturers
- · Inefficient LLM architectures
- · Developers focused solely on static optimizations
KnapSpec directly increases the throughput of LLM inference by optimizing layer selection.
Higher throughput and lower latency will enable more complex, real-time AI applications and make LLMs more economically viable for diverse use cases.
The widespread adoption of such efficiency optimizations could accelerate the development and deployment of scaled AI agentic systems due to reduced compute expenditure.
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Read at arXiv cs.LG