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Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading

NVIDIA Developer Blog · published 10 July 2026

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Host
https://github.com/jax-ml/jax
github.com
https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/
nvidia.com
https://docs.jax.dev/en/latest/gradient-checkpointing.html
docs.jax.dev
https://github.com/AI-Hypercomputer/maxtext
github.com
https://github.com/openxla/xla
github.com
https://www.nvidia.com/en-us/data-center/gb200-nvl72/
nvidia.com
https://ai.meta.com/blog/meta-llama-3-1/
ai.meta.com
https://arxiv.org/abs/2412.19437
arxiv.org
Introducing NVFP4 for Efficient and Accurate Low-Precision Inference Read at the source ↗
developer.nvidia.com
https://www.nvidia.com/en-us/data-center/technologies/rubin/
nvidia.com
https://developer.nvidia.com/nccl
developer.nvidia.com
https://developer.nvidia.com/cudnn
developer.nvidia.com
https://jax.readthedocs.io/en/latest/notebooks/host-offloading.html
jax.readthedocs.io
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/jax
catalog.ngc.nvidia.com
http://ghcr.io/nvidia/jax:deepseek_v3_maxtext
ghcr.io
https://github.com/NVIDIA/TransformerEngine
github.com
https://developer.nvidia.com/nsight-systems
developer.nvidia.com
https://github.com/NVIDIA/JAX-Toolbox
github.com