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arXiv Machine Learning By Martin Loretz, Sepp Hochreiter

Accelerating LLM Inference via Vector Index Based Output Embeddings

Read the original on arXiv Machine Learning →

The paper proposes replacing dense output projection in large language models with an HNSW-based vector index to perform maximum inner product search over token embeddings. This approach reduces memory bandwidth usage by retrieving only a small set of high-scoring tokens and can be integrated into existing decoding pipelines via sparse logits scattering. Experiments on Gemma 3, Llama 3.2, and Qwen 3 show up to 82% speed‑up in batch‑size‑one decoding while maintaining generation quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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