Optimizing Large Language Model Training Using FP4 Quantization
pith:6BBITQMB Add to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{6BBITQMB}
Prints a linked pith:6BBITQMB badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original abstract
The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to significant quantization errors and limited representational capacity. This work introduces the first FP4 training framework for LLMs, addressing these challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse. To ensure stability, the framework integrates a mixed-precision training scheme and vector-wise quantization. Experimental results demonstrate that our FP4 framework achieves accuracy comparable to BF16 and FP8, with minimal degradation, scaling effectively to 13B-parameter LLMs trained on up to 100B tokens. With the emergence of next-generation hardware supporting FP4, our framework sets a foundation for efficient ultra-low precision training.
This paper has not been read by Pith yet.
Forward citations
Cited by 9 Pith papers
-
Pretraining large language models with MXFP4 on Native FP4 Hardware
Weight-gradient quantization drives most convergence problems in MXFP4 pretraining of Llama 3.1-8B; deterministic Hadamard rotations stabilize training by correcting structured micro-scaling errors.
-
Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling
Four Over Six adaptively scales blocks in NVFP4 quantization to smaller FP4 values, making representable value distributions more uniform and reducing quantization error especially for near-maximal values.
-
Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention
Low-precision Flash Attention fails due to similar low-rank attention representations combined with biased rounding errors that accumulate and corrupt weight updates; a minimal fix to reduce rounding bias stabilizes training.
-
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA enables practical FP8 use in numerically sensitive large recommendation models via profiling, model adaptations, and runtime kernel orchestration.
-
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA enables practical FP8 use in numerically sensitive large recommendation models via online profiling of activations, reusable model modifications for stability, and dynamic kernel dispatching.
-
Pretraining large language models with MXFP4 on Native FP4 Hardware
Weight gradient quantization is the main driver of instability in full-pipeline FP4 LLM training, mitigated by deterministic Hadamard rotations rather than added stochasticity.
-
Pretraining large language models with MXFP4 on Native FP4 Hardware
Weight gradient FP4 quantization drives LLM pretraining divergence, which deterministic Hadamard rotations can stabilize on native MXFP4 hardware.
-
Normalized Architectures are Natively 4-Bit
nGPT's hypersphere constraint makes dot-product signal accumulate constructively under 4-bit quantization while noise averages out, enabling native low-precision training.
-
HiFloat4 Format for Language Model Pre-training on Ascend NPUs
HiFloat4 FP4 with stabilization techniques trains dense and MoE language models on Ascend NPUs at relative error within 1% of full-precision baselines.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.