One stop shop for running AI/ML on AWS
Docs · Available Images · Tutorials
AWS Deep Learning Containers (DLCs) are pre-built Docker images for running AI/ML workloads on AWS. Each image is tested and patched for security vulnerabilities. For more details, visit our documentation.
- [2026/08/17] SGLang Server v1.3 (AL2023) — EC2:
server-cuda-v1.3· SageMaker:server-sagemaker-cuda-v1.3· SGLang0.5.17(up from 0.5.14); Kimi-K3 (2.8T MoE, MXFP4) support; sgl-kernel 0.4.5, FlashInfer 0.6.15.post1, Mooncake 0.3.12.post1. - [2026/08/17] vLLM Server v2.3 (AL2023) — EC2:
server-cuda-v2.3· SageMaker:server-sagemaker-cuda-v2.3· vLLM0.27.0(up from 0.26.0); Kimi K3 (native support + kernels, Rust/Python frontends); FlashInfer 0.6.16.post3; NVIDIA B300 (SM103); new models K-EXAONE-2.0-750B-A37B, jina-embeddings-v5-text-nano, Qwen3.5; dynamic FP8 for Inkling; Baidu Unlimited-OCR smoke test. - [2026/08/14] vLLM v0.27.1 (Ubuntu) — EC2:
0.27.1-gpu-py312-ec2· SageMaker:0.27.1-gpu-py312· Kimi K3, Qwen3.5 dense + MoE (EVS video token pruning), K-EXAONE-2.0-750B-A37B, VaultGemma, jina-embeddings-v5-text-nano. - [2026/08/14] WhisperX v3.8.6 (AL2023) — EC2:
3.8.6-cu128-amzn2023· SageMaker:3.8.6-cu128-amzn2023-sagemaker· Initial release: speech transcription with word-level alignment (wav2vec2) and speaker diarization (pyannote) through an OpenAI-compatible API on CUDA 12.8 / Python 3.12; real-time and asynchronous SageMaker endpoints. - [2026/08/12] Ray v1.4 (2.57.0, AL2023) — EC2:
serve-ml-cuda-v1.4·serve-ml-cpu-v1.4· SageMaker:serve-ml-sagemaker-cuda-v1.4·serve-ml-sagemaker-cpu-v1.4· Ray2.57.0(up from 2.56.1). - [2026/08/08] SGLang v0.5.17 (Ubuntu) — EC2:
0.5.17-gpu-py312-ec2· SageMaker:0.5.17-gpu-py312· Kimi K3, MiniMax H3. - [2026/08/07] vLLM-Omni v1.5 (AL2023) — EC2:
omni-cuda-v1.5· SageMaker:omni-sagemaker-cuda-v1.5· vLLM-Omni0.26.0(up from 0.21.0rc1) on vLLM v0.26.0 with the new Rust frontend; SageMaker bidirectional WebSocket streaming (InvokeEndpointWithBidirectionalStream) for low-latency TTS and realtime sessions; FlashInfer 0.6.14; s3tokenizer bundled for CosyVoice3. - [2026/08/07] HuggingFace Text Embeddings Inference (TEI) v1.9.3 — SageMaker GPU:
2.0.1-tei1.9.3-gpu-py310-cu129-ubuntu24.04· SageMaker CPU:2.0.1-tei1.9.3-cpu-py310-ubuntu24.04· Migrated to the DLC release system; ECR account IDs are per-region (see Region Availability). - [2026/08/03] vLLM Server v2.2 (AL2023) — EC2:
server-cuda-v2.2· SageMaker:server-sagemaker-cuda-v2.2· vLLM0.26.0(up from 0.24.0); FlashInfer 0.6.15.post1; DeepEP EPv2/GIN backend (NCCL pinned to 2.30.7); Inkling (piecewise CUDA graph, MTP speculative decoding, LoRA, NVFP4), Cosmos3 Edge Reasoner, TranslateGemma-12b-it, BertForMaskedLM.
- [2026/04/28] We cannot guarantee security patching on Ubuntu-based vLLM and SGLang images due to the lack of Ubuntu Pro licensing. Customers may continue using these images at their own discretion and risk. We recommend migrating to our Amazon Linux-based images.
- [2026/02/10] Extended support for PyTorch 2.6 Inference containers until June 30, 2026
- PyTorch 2.6 Inference images will continue to receive security patches and updates through end of June 2026
- For complete framework support timelines, see our Support Policy
- Distributed Training on Amazon EKS - Configure and validate a distributed training cluster with DLCs on Amazon EKS.
- DLCs with Amazon SageMaker AI & MLflow - Use DLCs with SageMaker AI managed MLflow for experiment tracking and model management.
- LLM Serving on Amazon EKS with vLLM - Deploy and serve LLMs on Amazon EKS using vLLM DLCs.
- Fine-tuning Meta Llama 3.2 Vision - Fine-tune and deploy Llama 3.2 Vision for web automation using DLCs, Amazon EKS, and Amazon Bedrock.
- DLCs with Amazon Q Developer and MCP - Streamline deep learning environments with Amazon Q Developer and Model Context Protocol.
- LLM Deployment on Amazon EKS - Deploy and optimize LLMs on Amazon EKS using vLLM DLCs. See also: Sample Code
This project is licensed under the Apache-2.0 License.