Monat: September 2026

Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models now available on Amazon SageMaker JumpStart

Mistral AI’s Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models from the Ministral 3 family bring compact, vision-capable language models purpose-built for edge deployment and resource-constrained environments, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Ministral-3-3B-Instruct-2512 is engineered for ultra-lightweight edge deployment with multimodal understanding. Comprising a 3.4B language model and a 0.4B vision encoder, it fits in just 8GB of VRAM in FP8 while supporting a 256K-token context window. It offers vision analysis, multilingual instruction following across dozens of languages (including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic), strong system-prompt adherence, and native function calling with structured JSON output—all under the Apache 2.0 license.
Ministral-3-8B-Instruct-2512 delivers frontier-class capabilities comparable to its larger Mistral Small 3.2 24B counterpart in a compact 8B form factor. Built with an 8.4B language model and a 0.4B vision encoder, it fits in 12GB of VRAM in FP8 and features an interleaved sliding-window attention pattern for faster, memory-efficient inference. It shares the same vision, multilingual, agentic, and function-calling capabilities as its 3B sibling while offering stronger reasoning and generation performance.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Qwen3.6-35B-A3B-NVFP4 and Wan2.1-T2V-1.3B-Diffusers models now available on Amazon SageMaker JumpStart

NVIDIA’s Qwen3.6-35B-A3B-NVFP4 and Alibaba’s Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning agentic coding with long-context reasoning and lightweight text-to-video generation, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3.6-35B-A3B-NVFP4 is optimized for agentic coding, multimodal reasoning, and long-context understanding as the NVIDIA-quantized variant of Alibaba’s Qwen3.6-35B-A3B. This Mixture-of-Experts model contains 35B total parameters with only 3B activated per token (8 of 256 experts), supporting a 262K-token context window extendable to ~1M via YaRN scaling. Quantized to NVFP4 using NVIDIA’s ModelOpt framework, it preserves thinking across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines—all at a significantly reduced memory footprint.
Wan2.1-T2V-1.3B-Diffusers excels in text-to-video generation on consumer-grade hardware. Built on the diffusion transformer paradigm with a novel Video Variational Autoencoder (VAE), this 1.3B-parameter model generates high-quality, physics-consistent video clips from text prompts while requiring only 8.19 GB of VRAM. It can produce a 5-second 480p video on an RTX 4090 in approximately 4 minutes, making it one of the most accessible open-source video generation models available.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com