--- title: "Fine-tune Llama 3 with PyTorch FSDP and Q-Lora on Amazon SageMaker" url: https://daily.dev/posts/fine-tune-llama-3-with-pytorch-fsdp-and-q-lora-on-amazon-sagemaker-v80iyqug8 source_url: https://www.philschmid.de/sagemaker-train-deploy-llama3 type: article source: "Philipp Schmid" published: 2026-05-31T07:41:25.678Z updated: 2026-05-31T08:07:30.387Z tags: ["aws", "deep-learning", "pytorch", "llama"] reading_time: 8 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Fine-tune Llama 3 with PyTorch FSDP and Q-Lora on Amazon SageMaker **[Philipp Schmid](https://daily.dev/sources/philschmid)** · 8 min read · 0 upvotes · 0 comments ## Summary Step-by-step guide to fine-tuning Llama 3 70B on Amazon SageMaker using PyTorch FSDP (Fully Sharded Data Parallel) and Q-LoRA with Hugging Face TRL, PEFT, and Flash Attention v2. Covers environment setup, dataset preparation using the no_robots dataset, launching a SageMaker training job on ml.p4d.24xlarge (8x A100 GPUs), and deploying the fine-tuned model to a SageMaker endpoint using the Hugging Face LLM Inference DLC with TGI. Training Llama 3 70B for 2 epochs on 10k samples takes ~84 minutes and costs ~$50. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Scale LLM fine-tuning with Hugging Face and Amazon SageMaker AI](https://daily.dev/posts/scale-llm-fine-tuning-with-hugging-face-and-amazon-sagemaker-ai-4tkqty16y) · AWS · 1 upvotes · 0 comments --- Tags: [#aws](https://daily.dev/tags/aws), [#deep-learning](https://daily.dev/tags/deep-learning), [#pytorch](https://daily.dev/tags/pytorch), [#llama](https://daily.dev/tags/llama) [View this post on daily.dev](https://daily.dev/posts/fine-tune-llama-3-with-pytorch-fsdp-and-q-lora-on-amazon-sagemaker-v80iyqug8)