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Vast.ai runs a marketplace that connects GPU owners with users who need high-performance computing for AI and machine learning. Users browse available hardware, compare performance using the DLPerf scoring function, and rent compute through interruptible instances and spot auctions to save money. The platform aggregates offerings from individuals, data centers, and large providers, enabling a diverse, competitive marketplace while prioritizing security and regulatory compliance. This approach lets customers choose hardware that matches their needs and budget, rather than sticking to traditional cloud providers. Vast.ai’s goal is to make AI development more accessible and to improve the utilization of existing computing resources, contributing to cost efficiency and sustainability in tech.
Industries
Data & Analytics
Hardware
Enterprise Software
AI & Machine Learning
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
Los Angeles, California
Founded
2018
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Vast raises nearly $200M to become China's latest AI unicorn. Jun 01, 2026 - 07:04 A startup that didn't exist until 2023 just raised nearly $200 million and crossed the billion-dollar valuation threshold. Vast, a Beijing-based company building AI-powered 3D modeling tools, has become China's newest AI unicorn, adding another name to an increasingly crowded roster of generative AI companies jockeying for dominance in the world's second-largest economy. The company's core product, Tripo AI, translates text and image prompts into detailed 3D objects. That pitch has resonated with some serious names: clients include NetEase and Sony. The funding trail. Vast's path to unicorn status has been unusually fast, even by Chinese AI standards. The company closed a $50 million Series A round in March 2026, led by Alibaba Group and Hengxu Capital, with participation from Baidu Ventures and other investors. That single round pushed the company's valuation past the billion-dollar mark. Prior to that milestone, the startup had already pulled in tens of millions from a mix of government-linked and private investors, including the Beijing Artificial Intelligence Industry Investment Fund, Vitalbridge, and Fortune Capital. When you stack all the rounds together, the total funding approaches the $200 million figure. Who's behind it. Vast was founded by CEO Simon Song, who previously co-founded MiniMax, another Chinese AI unicorn. The company's user base tells its own story about product-market fit. Vast claims nearly 10 million individual users and roughly 90,000 studios and companies on the Tripo platform. The presence of enterprise clients like NetEase and Sony suggests the output quality is production-grade, or at least close enough that major studios are willing to integrate it into their workflows. The competitive landscape and what investors should watch. Vast positions itself as a competitor to efforts from Tencent and Google in text-to-3D generation. Tencent has the advantage of owning massive gaming and entertainment platforms that are natural distribution channels for 3D content. Disclosure: This article was edited by Editorial Team. For more information on how Ranzware inc. create and review content, see its Editorial Policy.
May 2026 product update. May 12, 2026 By Team Vast This month's updates bring new templates and guides, major upgrades to two-factor authentication, a benchmarking tool to help you pick the right GPU for your workload, and a complete creative AI studio that runs in a single container. NVIDIA Cloud GPU updates. Vast.ai Inc. has introduced several platform-level improvements this month. Two-factor authentication (2FA) now supports authenticator apps like Google Authenticator and 1Password instead of relying solely on SMS. Additional upgrades include CLI 2FA support, automatic backup codes for account recovery, multiple authentication methods per account, and 2FA for team roles and API key permission groups. Note: Action is required if you use legacy SMS 2FA. Simply log in normally, go to the Settings page, and click "Regenerate" on the backup codes section to migrate to the new 2FA in less than a minute. For developers, the Vast pip package has been upgraded, and the SDK and CLI repositories are now merged into a single, easy-to-grab repo. Vast.ai Inc. has also introduced an agent skill for coding assistants like Claude and Codex to drive its CLI more effectively. Highlighted feature: benchmark before you deploy. Choosing the right production GPU is now faster and easier, without any guesswork involved. Vast's benchmarking CLI will take your workload and test it on H100s, A100s, 5090s, 4090s, or any selection you define to compare performance and cost efficiency across GPU classes. The CLI rents one instance of each GPU type you specify, or a default set for a reasonable starting point if you don't specify any, and then runs the template's built-in benchmark workload, reports performance per dollar, and tears down each rental automatically when finished. Multiple GPU types can be tested in parallel, making it much faster to evaluate your options before committing to longer rentals or production traffic. Because each run uses real instances and consumes account credit, you get measured performance and cost per hour on actual hardware, not estimates. This benchmarking tool is built for the moment you're staring at a pricing page, wondering whether an H100 is actually worth 4x the cost of a 5090. Sometimes it is, and sometimes it isn't. Now you can find out before you deploy. New templates and guides. Vast.ai Inc. has rolled out another batch of templates and guides to support creative workflows, fine-tuning, experimentation, and multimodal reasoning. The standout addition this month is the All-in-One App Studio template. It packs eight creative AI applications and a GPU-accelerated remote desktop with KDE Plasma and Blender into a single container, giving you a complete AI production environment without having to manage separate instances. New templates. * All-in-One Creative AI Studio - One GPU instance: image generation, video, music, voice, transcription, LoRA training, LLM fine-tuning, and KDE desktop with Blender, each activated only when you need it. * Unsloth Studio - No-code UI for running and fine-tuning 500+ open-source models with 70% less VRAM. * Kimi K2.6 - 1T-parameter MoE, 32B active, multimodal agentic model with 256K context. * Qwen3.5 27B and Qwen 3.5B-A3B - MoE vision-language models with unified multimodal reasoning and 256K context. * Gemma 4 26B-A4B and Gemma 31B IT - Google's vision-language models with thinking mode and 256K context. * Autoresearch - Let Claude Code run ML experiments for you, now in a template. New guides. Other improvements. This update also brings fleet-wide mitigations for the recently disclosed CopyFail exploit. No action is required from users, and running workloads remain unaffected. Its commitment. As other clouds are out of GPU capacity or charging a premium for what's left, Vast.ai Inc. remain focused on making high-performance AI infrastructure available, affordable, and easy to deploy at scale. Need help? Contact Vast.ai Inc. anytime at [email protected] or join its Discord server for tips, community support, and platform updates in real time. Change log. New features. * Benchmarking CLI for GPU performance and cost comparison. * All-in-One App Studio: eight creative AI tools in one GPU instance. * Authenticator app support: use any TOTP app, including Google Authenticator, 1Password, and Microsoft Authenticator, instead of SMS. * CLI 2FA: 2FA now works through the Vast CLI. * Backup codes: automatically generate one-time recovery codes so you're never locked out. * Multiple authentication methods: register more than one per account. * 2FA for team roles and API key permission groups. Issues resolved. * CopyFail exploit: fleet-wide mitigations rolled out with no impact on running workloads and no user action required. * SSH key input validation, error message, and link fixes. * Cloud copy fixes. * Mobile UI fixes. * Notifications fixes. * General proactive security fixes. API changes. * The Vast pip package has been upgraded, and the SDK and CLI repos have been merged into a single, easy-to-grab repo. Use: pip install vastai. * Install its agent skill to help Claude, Codex, etc., drive its CLI: npx skills add vast-ai/vast-cli. Updated templates. * Wan2GP - migrated from provisioning installer to Docker. * Whisper UI - migrated from provisioning to Docker image. * FluxGym - Dockerized to prevent provisioning failures. * vLLM - updated build actions for new CU130 image tag.
Vast.ai named among fastest growing vendors by ramp and brex. March 5, 2026 By Team Vast
WAN 2.2 vs. LTX-2: which AI video model should you use? Wouldn't it be great if you could just think of a scene and instantly turn it into a video the way it appears in your head? Technology isn't quite there yet, but today's most advanced AI video generation models are getting Vast.ai Inc. closer. Today Vast.ai Inc. is taking a look at WAN 2.2 and LTX-2, two open-source/open-weights models that transform text and images into short-form video. What WAN 2.2 and LTX-2 are - and how they differ. From the outside, WAN 2.2 and LTX-2 are pretty similar tools. They're both open-source/open-weights diffusion-based video generation models designed to turn images or text prompts into short video clips. Their underlying architecture, however, is very different. WAN 2.2: prompt fidelity and Cinematic control. Developed by Alibaba Tongyi Lab, WAN 2.2 is built around a Mixture-of-Experts (MoE) architecture. Instead of using a single neural network to manage the entire denoising process, it employs two specialized "experts": a high-noise expert for overall structure and layout, and a low-noise expert for refining textures and details like lighting and color tone. Switching between these two experts means the model can allocate compute depending on what it needs to do at any given moment - focusing on broader structure first and finer details later. It also boosts efficiency by avoiding unnecessary computation. WAN 2.2 comes in three main variants, each designed for different workflows: * Text-to-Video (T2V): Generates 5-second video clips at 480P to 720P from a text prompt written in plain language. This is a flexible option for scenes where everything needs to be synthesized from scratch. * Image-to-Video (I2V): Begins with a single image and turns it into a short video. It uses automatic prompt derivation to generate video from an image without text input at all, but can also support text prompts for more directed results. * Hybrid: A compact model with 5 billion parameters that handles both text-to-video and image-to-video generation. It delivers high-definition results at up to 720P and 24 FPS, but is designed for users with lower VRAM. The base WAN 2.2 models generate video only, without native audio output. However, there is a specialized speech-to-video version (WAN 2.2 S2V) that transforms static images and audio inputs into synchronized videos. LTX-2: native audio-video generation. Created by Lightricks, LTX-2 is a DiT-based (Diffusion Transformer) audio-video generation model. It produces audio and visuals together in one pass, keeping dialogue, lip movements, and ambient sound aligned coherently. Its architecture is based on latent diffusion, which means the model works in a compressed version of the video first, before converting it into full resolution. This makes it more memory efficient and enables faster iteration, translating to quicker experimentation and lower hardware overhead. LTX-2 can generate up to ~20 seconds of synchronized audio and video, with support for high resolutions and high frame rates depending on configuration and available compute. The model offers fine-grained control options - such as LoRA-based customization and multimodal inputs including text, image, video, and audio - for precise creative direction. This makes LTX-2 a highly flexible model. In short, it supports text-to-video, image-to-video, and native audio-visual generation, along with cross-modal workflows like audio-to-video, text-to-audio, and video-to-audio - all within a single model. Choosing the right model for your workflow. How the two models are designed directly affects what you experience as a user. For instance, WAN 2.2's MoE design prioritizes structured generation and motion consistency. It boasts strong prompt adherence with high-fidelity output and is more likely to preserve scene intent across frames, sticking closely to what you asked for - albeit at the cost of slightly longer generation times. LTX-2's latent diffusion approach emphasizes speed and accessibility. It's faster to iterate with, easier to experiment on, and even offers native audio-video sync. However, it may require more prompt tuning to get exactly what you want. Choose WAN 2.2 if you want: * Cinematic or narrative-style clips where composition and camera motion are critical * Strong prompt fidelity for complex scenes with multiple elements * More deliberate, production-oriented outputs and professional video content Choose LTX-2 if you prefer: * Rapid prototyping of video concepts and creative exploration for lengthier scenes * Visual storytelling or character-driven video with synchronized dialogue or sound * A lighter and more iterative workflow where speed matters more than precision Both models also integrate with ComfyUI, so you can jump right into testing them out with an intuitive node-based visual workflow. Final thoughts. Neither WAN 2.2 nor LTX-2 is objectively superior to the other. The two open-source/open-weights models are designed for different kinds of workflows and creative goals. The best way to get a feel for them is to actually try them out. The good news is that both models run well on high-end consumer GPUs, making them far more accessible than many people might expect. With Vast.ai, it's even easier: you can spin up the right hardware on demand and experiment on your own terms, paying only for the compute you need - and save up to 80% over traditional clouds. Try WAN 2.2 T2V and WAN 2.2 I2V, or LTX-2 (or both!) in its Model Library, and build your own creative pipeline on Vast.ai today.
3D model company VAST has secured a multi-million dollar Pre-A+ funding round, led by the Beijing AI Industry Investment Fund, with participation from Jingya Capital. Previous investors include Oasis Capital, Fortune Capital, Primavera Capital, Inno Angel Fund, and Tsinghua Alumni Seed Fund. VAST has also launched Tripo Studio, the world's first AI-driven one-stop 3D workstation, and plans to release a new algorithm, Tripo 3.0.
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Industries
Data & Analytics
Hardware
Enterprise Software
AI & Machine Learning
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
Los Angeles, California
Founded
2018
Find jobs on Simplify and start your career today