Megatron-LM splits models across GPUs for training
A 2019 NVIDIA paper describes using model parallelism to train language models with billions of parameters by distributing weights across GPUs when they do not fit on one.
Source: Megatron-LM usa model parallelism para treinar grandes modelos de linguagem (arxiv.org). Text prepared with AI from this source.
What happened and what to do
NVIDIA’s 2019 paper, presented on the page on April 23, 2026, describes using model parallelism to train language models with billions of parameters. The technique splits model weights across GPUs, making training possible when they do not fit on a single GPU.
A company considering training its own models could assess memory and infrastructure requirements and test a distributed setup before expanding its investment. This work could include metrics collection, training monitoring, and dashboards for tracking GPU usage and experiment progress.
How the consultancy can help
Wendelmaques can assess training requirements and available infrastructure, propose a scoped distributed implementation, and support the operation of metrics collection and monitoring.
Next step
Send a short description of your model, infrastructure, and training goal to receive a scoped proposal.
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