7 Popular Model Templates for AI Workloads on Vast.ai Right Now
When you're getting started with an AI project, choosing the right model is only half the battle. You also need to configure environments and install dependencies while connecting the tools that make your workflows run smoothly. Setting things up can quickly become a bottleneck.
Prebuilt templates simplify the process – and Vast.ai gives you plenty to choose from!
Our Model Library offers a model-first way to deploy AI, with templates that launch ready-to-run environments for some of the most-used AI tools today. Vast.ai also offers lower-level templates for frameworks and inference engines like PyTorch and vLLM for more customizable setups.
Each pre-configured template is organized by modality and use case so you can quickly find what you need – and you retain full custom control over the underlying runtime, parameters, and infrastructure.
Popular Templates in the Vast.ai Model Library
From image, text, and audio-video generation to multimodal AI workflows, the following are a few of the most popular templates being deployed on Vast.ai right now.
1. Flux.2-Dev – Rectified Flow Image Generation
FLUX.2-Dev is a 32-billion parameter rectified flow transformer built for advanced text-to-image generation and editing. The model processes text prompts alongside optional reference images, so you can modify existing images or combine multiple visual references without additional fine-tuning.
Its ability to maintain consistent characters, objects, and styles across generations makes it ideal for character design, visual storytelling with scene continuity, digital art with precise style control, and design prototyping.
2. LTX-2 – Native Audio-Video Generation in a Single Workflow
LTX-2 is a 19-billion parameter Diffusion Transformer (DiT) model that generates audio and video together in a single pass. It creates synchronized audiovisual output from text prompts, images, and other inputs – keeping dialogue, motion, and ambient sound aligned throughout the clip.
One of LTX-2's biggest advantages is its light and iterative workflow. Since the model generates video in a compressed latent space before converting it to full resolution, it supports faster iteration and more efficient memory usage than many traditional video models.
3. WAN 2.2 – Efficient Text-to-Video and Image-to-Video Generation
WAN 2.2 is a video generation model that produces short cinematic clips from either text prompts or static images. What sets it apart is its Mixture-of-Experts (MoE) architecture. The model switches between specialized experts that handle early scene structure and later visual refinement. This results in smoother motion and higher visual fidelity along with more efficient use of compute.
Available on Vast.ai in both text-to-video (T2V) and image-to-video (I2V) variants – with the latter offering optional guidance via text prompts – WAN 2.2 gives you fine control over motion, lighting, and scene composition. Popular use cases include rapid prototyping of video concepts based on text descriptions and product animations generated from still images.
Not sure whether WAN 2.2 or LTX-2 fits your workflow best? Our detailed comparison post explores the strengths of each model and which one to choose for your specific needs!
4. ACE-Step V1 – Fast Text-to-Music Generation
ACE-Step V1 is an open-source music generation model that creates full musical compositions from text prompts in 17 languages. Thanks to its diffusion-based architecture and efficient audio compression pipeline, it generates music about 15x faster than comparable alternatives.
Musical quality is high, with controllable duration (up to five minutes works best) and coherent output across melody, harmony, and rhythm. You can even edit lyrics and manipulate vocals. Overall, ACE-Step V1 is a practical tool for fast experimentation with musical ideas, as well as voice cloning applications and music remixing and style transfer.
5. Dia 1.6B – Realistic Dialogue Generation from Text
Dia 1.6B is a text-to-speech model that produces highly realistic dialogue in English from given transcripts. Using [S1] and [S2] speaker tags, it creates multi-speaker conversations that can also include nonverbal expressions like laughter, coughing, and gasps.
Audio conditioning enables precise control over emotion and tone, and the model even supports voice cloning functionality. Dia 1.6B's applications include conversational audio content, accessibility tools, and voice synthesis applications that require consistent, natural-sounding speech.
6. DeepSeek V3.2 Exp – Long-Context Sparse Attention LLM
DeepSeek V3.2 Exp is an open-source experimental large language model designed for long-context reasoning and advanced text generation. It introduces DeepSeek Sparse Attention, a novel mechanism that enables efficient processing of extended documents and conversations without sacrificing output quality.
The model is well suited for long-form document analysis and generation, research that requires extended reasoning, code generation and debugging, and even customer support with context-aware responses. Notably, it has a license that offers full commercial use permissions.
7. Qwen3.5 397B A17B – Multimodal Reasoning and Agentic Workflows
Qwen3.5 397B A17B is the open-weight of the first model in the new Qwen3.5 series. It's a frontier-level native vision-language model with outstanding results across numerous benchmarks.
Built with a sparse mixture-of-experts (MoE) architecture and hybrid attention mechanisms, Qwen3.5 397B A17B supports highly complex tasks in areas like mathematical reasoning and problem solving, multi-turn agentic workflows, code generation and debugging, document analysis, video understanding, visual question answering, and multilingual interaction across 200+ languages and dialects.
Launch Templates in Minutes on Vast.ai
Today's AI workloads span an incredibly wide range of modalities. The templates above represent just a small snapshot of what developers are building with on Vast.ai right now.
With the Vast.ai Model Library, getting started on your own AI projects is easy. Simply browse templates by use case, select the recommended GPU hardware, and launch a production-ready environment in minutes!
