Puzzle: Distillation-Based NAS for Inference-Optimized LLMs
Authors: name: Akhiad Bercovich, name: Tomer Ronen, name: Talor Abramovich, name: Nir Ailon, name: Nave Assaf, name: Mohammad Dabbah, name: Ido Galil, name: Amnon Geifman, name: Yonatan Geifman, name: Izhak Golan, name: Netanel Haber, name: Ehud Karpas, name: Itay Levy, name: Shahar Mor, name: Zach Moshe, name: Najeeb Nabwani, name: Omri Puny, name: Ran Rubin, name: Itamar Schen, name: Ido Shahaf, name: Oren Tropp, name: Omer Ullman Argov, name: Ran Zilberstein, name: Ran El-Yaniv

Large language models (LLMs) have demonstrated remarkable capabilities, but
their adoption is limited by high computational costs during inference. While
increasing parameter counts enhances accuracy, it also widens the gap between
state-of-the-art capabilities and practical deployability. We present Puzzle, a
framework to accelerate LLM inference on specific hardware while preserving
their capabilities. Through an innovative application of neural architecture
search (NAS) at an unprecedented scale, Puzzle systematically optimizes models
with tens of billions of parameters under hardware constraints. Our approach
utilizes blockwise local knowledge distillation (BLD) for parallel architecture
exploration and employs mixed-integer programming for precise constraint
optimization.
  We demonstrate the real-world impact of our framework through
Llama-3.1-Nemotron-51B-Instruct (Nemotron-51B), a publicly available model
derived from Llama-3.1-70B-Instruct. Nemotron-51B achieves a 2.17x inference
throughput speedup, fitting on a single NVIDIA H100 GPU while preserving 98.4%
of the original model's capabilities. Nemotron-51B currently stands as the most
accurate language model capable of inference on a single GPU with large batch
sizes. Remarkably, this transformation required just 45B training tokens,
compared to over 15T tokens used for the 70B model it was derived from. This
establishes a new paradigm where powerful models can be optimized for efficient
deployment with only negligible compromise of their capabilities, demonstrating
that inference performance, not parameter count alone, should guide model
selection. With the release of Nemotron-51B and the presentation of the Puzzle
framework, we provide practitioners immediate access to state-of-the-art
language modeling capabilities at significantly reduced computational costs.