LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
Training large neural networks with constant memory using a new execution algorithm
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ZeRO removes memory redundancies in parallel training to scale deep learning models to over a trillion parameters with high throughput on current hardware.
A survey that frames data selection, memory optimization, and compute budgeting as coupled bottlenecks in LLM training rather than isolated techniques.
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LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
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ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
ZeRO removes memory redundancies in parallel training to scale deep learning models to over a trillion parameters with high throughput on current hardware.
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Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey
A survey that frames data selection, memory optimization, and compute budgeting as coupled bottlenecks in LLM training rather than isolated techniques.