I'm building a fine-tuning pipeline and need GPU guidance

Absolutely — I can help with GPU guidance for a fine-tuning pipeline.

To make the recommendations useful, I’d want a few details:

• Model size: e.g. 7B, 13B, 70B, ViT, etc.
• Fine-tuning method: full fine-tune, LoRA/QLoRA, adapters, prompt tuning
• Framework: PyTorch, TensorFlow, JAX, Hugging Face, DeepSpeed, FSDP
• Precision: FP32, FP16, BF16, INT8/4-bit
• Batch/sequence length: especially for LLMs
• Throughput goals: training speed vs cost efficiency
• Hardware constraints: single GPU, multi-GPU, cloud instance type, budget

A good rule of thumb:

• LoRA / QLoRA for 7B models: often workable on 1×24GB GPU, sometimes less with careful settings.
• Full fine-tuning 7B+: usually needs 48GB+, and often multi-GPU depending on sequence length and optimizer choice.
• 13B models:
  • QLoRA: commonly 1×24GB or 1×48GB
  • Full fine-tune: typically multi-GPU / high-memory GPUs
• 70B models: generally multi-GPU, usually with tensor/pipeline parallelism and high VRAM per GPU.

If you want, I can help you in one of these ways:

1. Recommend a GPU based on your model/setup
2. Estimate VRAM usage
3. Compare GPU options for cost/performance
4. Suggest memory-saving training settings
5. Design a multi-GPU setup

If you share your model size and whether you’re doing LoRA/QLoRA or full fine-tuning, I can give you a concrete GPU recommendation.