LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs

• URL: http://arxiv.org/abs/2512.15306v1
• Date: Wed, 17 Dec 2025 10:51:45 GMT
• Title: LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs
• Authors: Erik Schultheis, Dan Alistarh,
• Abstract summary: We present an end-to-end/C++ implementation for medium-sized language-model training, e.g. 3B to 32B parameters, on commodity GPUs.<n>This is achieved while executing a standard 8-bit training pipeline, without additional algorithmic approximations, and maintaining FLOP utilization of around 50%.
• Score: 45.51664355320938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present LLMQ, an end-to-end CUDA/C++ implementation for medium-sized language-model training, e.g. 3B to 32B parameters, on affordable, commodity GPUs. These devices are characterized by low memory availability and slow communication compared to datacentre-grade GPUs. Consequently, we showcase a range of optimizations that target these bottlenecks, including activation checkpointing, offloading, and copy-engine based collectives. LLMQ is able to train or fine-tune a 7B model on a single 16GB mid-range gaming card, or a 32B model on a workstation equipped with 4 RTX 4090s. This is achieved while executing a standard 8-bit training pipeline, without additional algorithmic approximations, and maintaining FLOP utilization of around 50%. The efficiency of LLMQ rivals that of production-scale systems on much more expensive cloud-grade GPUs.

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