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Model Fine-Tuning

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    Set up Infrastructure
    Set up Infrastructure
    Updated on 05 Nov 2025
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    We support both single-node and multi-node configurations, with a maximum of 16 nodes:

    Alt text

    We recommend you scalable the right infrastructure below:

    • Number of GPUs depends on the model size:

      • <1B parameters: 1 GPU (2GB VRAM) is sufficient

      • 7B parameters: 2-4 GPUs (40GB VRAM each)

      • 13B parameters: 4-8 GPUs recommended

      • 30B+ parameters: Requires 8+ GPUs and multi-node setup

    • When to use single-node or multi-node:

      • For small to medium models (up to 13B), a single-node with multiple GPUs is enough

      • For large models (30B+), multi-node setups are recommended for better memory and performance

    • The minimum GPU memory required:

      • At least 24GB per GPU for standard fine-tuning.

      • You can fine-tune on GPUs with 8-16GB VRAM using LoRA or QLoRA methods.

    Example:

    Model: Llama-3.1-8B-Instruct

    • Training type: Full

      • Number of GPUs: can fit into 2 GPUs (nearly 99% usage) -> 4 GPUs for more consistent runtime

      • Distributed backend: DeepSeed

      • ZeRO stage: 3

      • Batch size per device: 1

      • All other parameters can be left as default

    • Training type: LoRA

      • Number of GPUs: can fit into 1 GPU

      • LoRA rank: 16

      • Batch size per device: 1

      • All other parameters can be left as default

    • To calculate the most suitable training configuration, you can refer here: https://rahulschand.github.io/gpu_poor/ (overhead 10-20%)

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