Muon

TL;DR for operators A small model can perform acceptably after task specialization and still lose more accuracy than expected when compressed. The paper examined here suggests that part of that loss may be shaped earlier, during fine-tuning, rather than being determined by the quantizer alone. Paper evidence: Muon-optimized models lost less accuracy after quantization than Adam-optimized models on six of eight benchmarks. On ARC-e, for example, accuracy fell by 0.55 percentage points after quantization with Muon versus 3.16 points with Adam. The integrated pipeline also produced a 2.86 GB quantized model from a 6.01 GB pre-quantized model while improving reported serving throughput and roughly halving per-token latency. ...

GPU memory is the part of AI strategy that does not care about adjectives. A team can say it is building a domain LLM, a private copilot, a long-context research assistant, or a fine-tuned enterprise model. The budget spreadsheet eventually asks a colder question: what actually fits on the available hardware? Model weights need memory. Gradients need memory. Activations need memory. Checkpoints need memory. And the optimizer — the quiet machinery that decides how parameters move during training — can require multiple additional copies of the model itself. ...