Training 20B Models at 7x Longer Context on 24GB GPUs
This article explains how researchers achieved training 20 billion parameter language models with seven times longer context windows using only 24GB GPUs
Someone found a way to train language models with 7x longer context windows using Unsloth’s new RL optimizations.
The setup:
- Install from https://github.com/unslothai/unsloth
- Combines three memory tricks: weight-sharing with vLLM, Flex Attention, and async gradient checkpointing
- Free notebooks at https://docs.unsloth.ai/get-started/unsloth-notebooks
What it actually does:
- Trains 20B models at 20K context on a 24GB GPU (normally impossible)
- Pushes Qwen3-8B to 110K context on an H100
- Works with Llama, Gemma, and other models out of the box
The wild part is combining all their features together - FP8 training, long context support, and memory-efficient RL all stack without breaking anything. Full benchmarks at https://unsloth.ai/docs/new/grpo-long-context if curious about the technical details.
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