LLMQ: Efficient lower-precision LLM training for consumer GPUs
E. Schultheis, D.-A. Alistarh, in:, 2nd Conference on Parsimony and Learning, ML Research Press, 2026, pp. 265–284.

Speculative decoding speed-of-light: Optimal lower bounds via branching random walks
S. Pankratov, D.-A. Alistarh, in:, Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 2026, pp. 6404–6418.

Panza: Investigating the feasibility of fully-local personalized text generation
A. Nicolicioiu, E.B. Iofinova, A. Jovanovic, E. Kurtic, M. Nikdan, A. Panferov, I. Markov, N. Shavit, D.-A. Alistarh, Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation, OpenReview, 2026.

Behemoth: Benchmarking unlearning in LLMs using fully synthetic data
E.B. Iofinova, D.-A. Alistarh, ArXiv (n.d.).

EvoPress: Accurate dynamic model compression via evolutionary search
O. Sieberling, D. Kuznedelev, E. Kurtic, D.-A. Alistarh, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 55556–55590.

Layer-wise quantization for quantized optimistic dual averaging
A.D. Nguyen, I. Markov, F.Z. Wu, A. Ramezani-Kebrya, K. Antonakopoulos, D.-A. Alistarh, V. Cevher, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 46026–46072.

An almost-logarithmic lower bound for leader election with bounded value contention
D.-A. Alistarh, F. Ellen, A. Fedorov, in:, 39th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2025, p. 3:1-3:16.

Sparse Fine-Tuning for Inference Acceleration of Large Language Models
E. Kurtic, D. Kuznedelev, E. Frantar, M. Goinv, S. Pandit, A. Agarwalla, T. Nguyen, A. Marques, M. Kurtz, D.-A. Alistarh, in:, P. Passban, A. Way, M. Rezagholizadeh (Eds.), Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques, Springer Nature, 2025, pp. 83–97.

Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence
S. Talaei, M. Ansaripour, G. Nadiradze, D.-A. Alistarh, Proceedings of the 39th AAAI Conference on Artificial Intelligence 39 (2025) 20778–20786.

MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models
E. Frantar, R.L. Castro, J. Chen, T. Hoefler, D.-A. Alistarh, in:, Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2025, pp. 239–251.

Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, Distributed Computing 38 (2025) 207–245.

Scalable mechanistic neural networks
J. Chen, D. Yao, A.A. Pervez, D.-A. Alistarh, F. Locatello, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 63716–63737.

LDAdam: Adaptive optimization from low-dimensional gradient statistics
T. Robert, M. Safaryan, I.-V. Modoranu, D.-A. Alistarh, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 101877–101913.

Wasserstein distances, neuronal entanglement, and sparsity
S. Sawmya, L. Kong, I. Markov, D.-A. Alistarh, N. Shavit, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 26244–26274.

The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws
T. Jin, A.I. Humayun, U. Evci, S. Subramanian, A. Yazdanbakhsh, D.-A. Alistarh, G.K. Dziugaite, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 85165–85181.

cGive me B or give me deathd? Accuracy-performance trade-offs in LLM quantization
E. Kurtic, A. Marques, S. Pandit, M. Kurtz, D.-A. Alistarh, in:, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2025, pp. 26872–26886.

Position: It's time to act on the risk of efficient personalized text generation
E.B. Iofinova, A. Jovanovic, D.-A. Alistarh, ArXiv (n.d.).

Hogwild! Inference: Parallel LLM generation via concurrent attention
G. Rodionov, R. Garipov, A. Shutova, G. Yakushev, E. Schultheis, V. Egiazarian, A. Sinitsin, D. Kuznedelev, D.-A. Alistarh, in:, 39th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025, pp. 52014–52055.

How to prune your language model: Recovering accuracy on the "Sparsity May Cry" benchmark
E. Kurtic, T. Hoefler, D.-A. Alistarh, in:, Proceedings of Machine Learning Research, ML Research Press, 2024, pp. 542–553.

Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.