arXiv:2606.10935v1 Announce Type: new Abstract: Large language model inference is bottlenecked by autoregressive decoding, where each token requires a full forward pass. Multi-token prediction (MTP) offers a promising acceleration path, but existing approaches suffer from a fundamental architectural flaw: the MTP head for the first token competes with the backbone's own language model (LM) head, leading to severe quality degradation when predictions are accepted. We identify this head-backbone competition as the root cause of repetitive and incoherent outputs in prior MTP-based acceleration me
The continuous drive to improve the efficiency and speed of large language models is leading to innovative solutions for their core limitations, such as autoregressive decoding.
Improving LLM inference speed directly impacts the cost and scalability of AI applications, making advanced AI more accessible and economically viable.
This research proposes a method to significantly accelerate LLM inference without quality degradation, addressing a major bottleneck in current AI deployment.
- · AI compute providers
- · Large language model developers
- · AI application developers
- · Cloud service providers
- · Inefficient LLM architectures
- · Companies reliant on current high inference costs
Faster LLM inference reduces computational costs and latency for AI services.
Lower costs could enable wider adoption of complex AI models, fostering new applications and services previously uneconomical.
Increased AI accessibility might accelerate the development of autonomous AI agents, further impacting various industries and white-collar workflows.
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Read at arXiv cs.LG