Current - Issue

1. Cai, T. et al. (2024). Medusa: Simple LLM inference acceleration framework with multiple decoding heads. International Conference on
Machine Learning (ICML).
2. Chen, C. et al. (2023). Accelerating large language model decoding with speculative sampling. arXiv:2302.01318.
3. Chen, T., Moreau, T., Jiang, Z., Zheng, L., Yan, E., Shen, H., Cowan, M., Wang, L., Hu, Y., Ceze, L., Guestrin, C., & Krishnamurthy, A.
(2018). TVM: An automated end-to-end optimizing compiler for deep learning. USENIX Symposium on Operating Systems Design and
Implementation (OSDI).
4. Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2022). LLM.int8(): 8-bit matrix multiplication for transformers at scale. Advances
in Neural Information Processing Systems, 35.
5. Elhoushi, M., Shrivastava, A., Liskovich, D., Hosmer, B., Wasti, B., Lai, L., Mahmoud, A., Acun, B., Agarwal, S., Roman, A., Aly, A., Chen,
B., & Wu, C.-J. (2024). LayerSkip: Enabling early exit inference and self-speculative decoding. Annual Meeting of the Association for
Computational Linguistics (ACL).
6. Fedus, W., Zoph, B., & Shazeer, N. (2022). Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.
Journal of Machine Learning Research, 23(120), 1–39.
7. Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D. (2023). GPTQ: Accurate post-training quantization for generative pre-trained
transformers. International Conference on Learning Representations (ICLR).
8. Frantar, E., Castro, R. L., Chen, J., Hoefler, T., & Alistarh, D. (2024). MARLIN: Mixed-precision auto-regressive parallel inference on large
language models. arXiv Preprint arXiv:2408.11743.
9. Fu, Y., Bailis, P., Stoica, I., & Zhang, H. (2024). Break the sequential dependency of LLM inference using lookahead decoding. International
Conference on Machine Learning (ICML).
10. Gerganov, G. et al. (2023). Llama.cpp: LLM inference in C/C++. https://github.com/ggml-org/llama.cpp.
11. He, Z., Zhong, Z., Cai, T., Lee, J. D., & He, D. (2024). REST: Retrieval-based speculative decoding. Proceedings of NAACL.
12. Kwon, W. et al. (2023). Efficient memory management for large language model serving with PagedAttention. Proceedings of the 29th
Symposium on Operating Systems Principles (SOSP).
13. leejet, & contributors. (2025). Stable-diffusion.cpp. https://github.com/leejet/stable-diffusion.cpp.
14. Leviathan, Y., Kalman, M., & Matias, Y. (2023). Fast inference from transformers via speculative decoding. International Conference on
Machine Learning (ICML).
15. Li, M., Lin, Y., Zhang, Z., Cai, T., Li, X., Guo, J., Xie, E., Meng, C., Zhu, J.-Y., & Han, S. (2025). SVDQuant: Absorbing outliers by lowrank
components for 4-bit diffusion models. International Conference on Learning Representations (ICLR).
16. Li, Y., Wei, F., Zhang, C., & Zhang, H. (2024). EAGLE: Speculative sampling requires rethinking feature uncertainty. International
Conference on Machine Learning (ICML).
17. Li, Y., Wei, F., Zhang, C., & Zhang, H. (2025). EAGLE-3: Scaling up inference acceleration of large language models via training-time test.
arXiv Preprint arXiv:2503.01840.
18. Lin, J. et al. (2024). AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration. Proceedings of Machine
Learning and Systems (MLSys).
19. Liu, J., Ponnusamy, P., Cai, T., Guo, H., Kim, Y., & Athiwaratkun, B. (2025). Training-free activation sparsity in large language models.
International Conference on Learning Representations (ICLR).
20. Liu, X., Daniel, C., Hu, L., Kwon, W., Li, Z., Mo, X., Cheung, A., Deng, Z., Stoica, I., & Zhang, H. (2024). Optimizing speculative decoding
for serving large language models using goodput. arXiv Preprint arXiv:2406.14066.
21. Liu, Z. et al. (2023). Deja vu: Contextual sparsity for efficient LLMs at inference time. International Conference on Machine Learning (ICML).
22. Ollama. (2023). Ollama: Get up and running with large language models. https://github.com/ollama/ollama.
23. Pope, R. et al. (2023). Efficiently scaling transformer inference. Proceedings of Machine Learning and Systems (MLSys).
24. Saxena, A. (2023). Prompt lookup decoding.
25. SGLang Team. (2025). SpecForge: Training speculative decoding models for SGLang.
26. Shazeer, N. et al. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. International Conference on
Learning Representations (ICLR).
27. Shim, K., Lee, M., Choi, I., Boo, Y., & Sung, W. (2017). SVD-softmax: Fast softmax approximation on large vocabulary neural networks.
Advances in Neural Information Processing Systems.
28. Tongyi Lab. (2025). Z-Image-Turbo. https://huggingface.co/Tongyi-MAI/Z-Image-Turbo.
29. Zheng, L. et al. (2024). SGLang: Efficient execution of structured language model programs. Advances in Neural Information Processing
Systems (NeurIPS).