
With so many AI model libraries to choose from, making the right decision requires research and testing. Luckily, [Vast.ai](http://vast.ai) partners with some of the leading innovators across a variety of generative and assistive models. Along with our GPU rentals, we empower businesses and individuals to produce their best work without latency issues or breaking the bank. Not only are these AI models reliable, they are also very affordable. You can scale production and workload with ease. Whether looking for an image generator, video production, audio, text or computer vision research, these are some of the best AI models available.

Model Library is Vast's new, model-first way to deploy AI. Instead of starting from the runtime-level (like vLLM, ComfyUI, or PyTorch), you can browse and discover model-level templates, sorted by modality and use case, then launch a running instance in seconds with full custom control over the underlying runtime, parameters, and infrastructure.

Every template includes recommended hardware presets and clear dedicated GPU rental pricing, so you can quickly get started with the right cost/performance setup and deploy with confidence, or tweak to fit your needs.

Coming soon: more runtimes, more model variations, and Vast Serverless model templates.

## The Best AI Image Generation Models

With text-based prompts, you can produce images from a range of popular AI models. From hyper-realistic renderings to cartoonish depictions, these models are among your best bets for generating images.

- **[HiDream I1 Full](https://vast.ai/model/hidream-i1-full)**: Released in May 2025, HiDream produces images in a variety of styles. Whether you want realistic photo imagery or stylized cartoon graphics, HiDream can deliver. With speed and precision, it can produce images without sacrificing quality.

- **[Qwen Image (FP8)](https://vast.ai/model/qwen-image)**: With a specific focus on generating images containing text, Qwen Image can produce high-quality images that provide a strong eye for detail. This is true not only of images with English characters, but even intricate characters for multilingual projects. It has a particular focus on generating images containing Chinese characters.

- **[FLUX.1 [dev]](https://vast.ai/model/flux.1-dev)**: Whether prototyping concepts, marketing, or digital art, FLUX.1 is an open-source tool that rivals many closed-source competitors in its functionality and precision. Developed by Black Forest Labs, this 12-billion-parameter image generator serves for a range of use cases from concepting to marketing materials and scientific visualization.

- **[Juggernaut XI v11](https://vast.ai/model/juggernaut-xi-v11)**: With a particular emphasis on photorealistic images, Juggernaut XI v11 is trained to provide exceptional detail on challenging human features like hands and facial expressions. It also provides exceptional detail for landscapes and other detailed imagery that have historically fallen into the realm of "uncanny valley."

- **[RealVisXL V5.0](https://vast.ai/model/realvisxl-v5.0)**: Developed by Evgeny, RealVisXL renders photorealistic images and produces anatomically accurate and high-fidelity visuals thanks to inference parameters like DPM++ SDE Karras sampling. This makes it equally adept at producing architectural designs and photorealistic images.

- **[Stable Diffusion XL Base 1.0](https://vast.ai/model/stable-diffusion-xl-base-1.0)**: Developed by Stability AI, Stable Diffusion XL Base improves image quality by providing two models. With a brainstorming mode in its base model, you can parse through a variety of images and then hone in on specific imagery with the more detailed refinement module. This holistic approach provides you with everything you need to craft the specific images you need.

## The Best AI Video Generation Models

[Vast.ai](http://vast.ai) offers some of the best video generation options available on the market. With options from a variety of AI engines and developers, you can produce stunning videos that rival cost-and-labor-intensive CGI and video productions.

- **[LTX Video](https://vast.ai/model/ltx-video)**: The first Diffusion Transformer (DiT)-based video generation model, LTX Video, produces high quality videos at resolutions and framerates that exceed much of the competition. Videos are available at 30 FPS and a 1216×704 resolution. This gives it an advantage over many of its competitors.

- **[Mochi 1 Preview](https://vast.ai/model/mochi-1-preview)**: Specializing in photorealistic video generation, Mochi 1 has 10 billion parameters that are built on an innovative Asymmetric Diffusion Transformer architecture. This helps it process over 44,000 visual tokens to produce high-quality videos.

- **[Wan2.2 I2V A14B (FP8)](https://vast.ai/model/wan-2.2-i2v-a14b)**: Wan2.2 I2V generates 720p videos from static image prompts. With a high-noise expert option for background imagery and a low-noise expert option for movement, you can make still images come to life with striking detail.

- **[Wan2.2 T2V A14B (FP8)](https://vast.ai/model/wan-2.2-t2v-a14b)**: Released in July 2025, Wan2.2 T2V generates 5-second videos at both 480P and 720P resolutions. With cinematic aesthetics and complex motion capabilities, it provides greater detail than many previous open-source and commercial models.

## The Best Audio Generation AI Models

Whether you need to produce a podcast, narrate an audiobook, engineer music production, or just explore the possibilities of ai-generated sounds, [Vast.AI](http://vast.ai) provides affordable audio generation options from some of the best-known models available.

- **[ACE Step V1 3.5B](https://vast.ai/model/ace-step-v1-3.5b)**: ACE Step V1 produces realistic audio thanks to its 3.5-billion-parameter model. It can be leveraged for a range of use cases: podcasts, audiobooks, and a wide range of music production prompts.

- **[Dia 1.6B](https://vast.ai/model/dia-1.6b)**: Developed by Nari Labs, Dia 1.6B is an audio generation model that specializes in multi-speaker production. This works great for podcasts and other text-to-audio prompts and excels at inflecting speakers' voices and producing sighs, laughter, and other non-verbal flourishes.

## The Best Text Generation AI Models Available

Whether you need agentic support, assistance on scientific research, or multilingual text translation, there are many incredible choices available. These are the best text generation AI models available from [Vast.AI](http://vast.ai).

- **[GLM 4.6](https://vast.ai/model/glm-4.6)**: This model provides support for language models that work on multi-step workflows. With over 200,000 tokens and sophisticated reasoning logic, it can excel at anything from coding applications to agentic capabilities.

- **[Kimi K2 Thinking](https://vast.ai/model/kimi-k2-thinking)**: Released under a Modified MIT License, Kimi K2 Thinking supports both commercial and research applications. Its chain-of-thought reasoning allows it to operate through hundreds of steps without degrading its response quality.

- **[DeepSeek V3.2 Exp](https://vast.ai/model/deepseek-v3.2-exp)**: Built for complex research and debugging scenarios, DeepSeek V3.2 Exp is an incredible assistant for very labor-intensive processes. With the ability to parse and analyze large bodies of text, you can automate some of the most tedious parts of your day in seconds.

- **[Kimi K2 Instruct 0905](https://vast.ai/model/kimi-k2-instruct-0905)**: With over a trillion parameters, Kimi K2 Instruct excels at language models including agentic intelligence and software development. Its massive 256k token window provides advanced decision-making capabilities for a range of issues and use cases.

- **[Qwen3 235B A22B Thinking 2507](https://vast.ai/model/qwen3-235b-a22b-thinking-2507)**: Developed by Alibaba, this 235-billion-parameter Mixture-of-Experts (MoE) language model is specifically engineered for extended reasoning tasks by activating 8 experts and 22 billion parameters per token. This makes it ideal for complex projects including mathematics, scientific research, and code generation.

- **[Qwen3 Coder 480B A35B Instruct](https://vast.ai/model/qwen3-coder-480b-a35b-instruct)**: This 480-billion-parameter Mixture-of-Experts (MoE) model developed by Alibaba is designed for advanced code generation and is optimized for actionable code output by operating exclusively in an efficient non-thinking mode.

- **[DeepSeek R1 0528](https://vast.ai/model/deepseek-r1-0528)**: This advanced reasoning language model developed by DeepSeek AI utilizes advanced chain-of-thought logic for complex mathematical and logical problems. This makes it a great choice for tool and API integrations.

- **[DeepSeek V3.1](https://vast.ai/model/deepseek-v3.1)**: This hybrid language model operates in both thinking and non-thinking modes. This dual-mode architecture is equally suited for deep reasoning with visible thought processes or generating fast responses without intermediate reasoning.

- **[GPT-OSS-120b](https://vast.ai/model/gpt-oss-120b)**: GPT-OSS-120b is an open-weight reasoning model from OpenAI designed for production use cases. With a range of low, medium, and high reasoning settings it balances speed and accuracy for agentic functions.

- **[GPT OSS 20b](https://vast.ai/model/gpt-oss-20b)**: Built for lower latency and specialized use cases. For everything from web browsing to function calling and API integration tasks, GPT OSS 20b is a great choice for agentic support.

- **[Llama 4 Maverick 17B 128E Instruct](https://vast.ai/model/llama-4-maverick-17b-128e-instruct)**: With seamless processing of both text and visual inputs, Llama 4 Maverick 17B supports up to a 10 million token context length and can process up to 5 input images simultaneously. It supports 12 languages for a wide array of agentic functions including English, Spanish, Tagalog, French, German, Hindi, Indonesian, Italian, Portuguese, Arabic, Thai, and Vietnamese.

- **[Llama 4 Scout 17B 16E Instruct](https://vast.ai/model/llama-4-scout-17b-16e-instruct)**: Released in April 2025, Scout 17B 16E Instruct employs a mixture-of-experts (MoE) and has a total of 109 billion parameters. It functions as a great assistant for analyzing data, parsing large quantities of data and is compatible with 12 languages including English, Spanish, Tagalog, French, German, Hindi, Indonesian, Italian, Portuguese, Arabic, Thai, and Vietnamese.

- **[Qwen3 VL 235B A22B Instruct](https://vast.ai/model/qwen3-vl-235b-a22b-instruct)**: The most powerful vision-language model in the Qwen series, VL 235B A22B Instruct combines 236 billion parameters across visual understanding, agent applications, context processing, and multimodal reasoning tasks. It excels at recognizing a range of items from landmarks, to plants and animals, to works of art and celebrities. This makes it a great research tool for imaging, robotics, and even technical documentation.

## The Best Computer Vision Models Available

When it comes to choosing an AI model that can interpret visual elements from real-world scenarios, these are among the best:

- **[DeepSeek OCR](https://vast.ai/model/deepseek-ocr)**: This vision-language model specializes in optical character recognition and document understanding. This makes it great for extracting text from images or scanned documents, processing PDFs, and much more.

- **[GLM 4.5V](https://vast.ai/model/glm-4.5v)**: Whether summarizing longform videos, analyzing data, or scanning documents for text, GLM 4.5V provides support for advanced reasoning tasks. With so many use cases, you can even toggle between quick responses or deeper analysis of data to make it work for you the way you want.

- **[InternVL3 78B](https://vast.ai/model/internvl3-78b)**: Whether analyzing technical documentation, interpreting results from medical images, or providing agentic support, InternVL3 78B has many use cases from text-only applications to a mixture of visual applications.

- **[Llama 4 Maverick 17B 128E Instruct](https://vast.ai/model/llama-4-maverick-17b-128e-instruct)**: Featuring 17 billion activated parameters distributed across 128 total experts, this model combines text and image understanding capabilities within a unified architecture. This model provides everything from an assistant-like conversational experience to image captioning, document analysis, and multilingual support.

- **[Llama 4 Scout 17B 16E Instruct](https://vast.ai/model/llama-4-scout-17b-16e-instruct)**: With efficiency being front and center, this model runs much more leanly than Maverick. This gives it a more conversational tone while still providing agentic support, robust analysis of charts, multilingual functionality.

- **[Qwen3 VL 235B A22B Instruct](https://vast.ai/model/qwen3-vl-235b-a22b-instruct)**: Qwen3's most powerful vision-language model, it provides incredible PC and mobile GUI navigation for automation tasks, visual coding support across a range of coding languages, and agentic support.

---

If you're ready to take your AI workflows to the next level, [Vast.ai](http://vast.ai) is here to help. From an [AI model library](https://vast.ai/model-library) with a host of tools and use cases to [GPU servers](https://cloud.vast.ai/) that can accelerate your research, we're here to help empower and scale your discovery every step of the way.

---


Explore Vast.AI's comprehensive model library featuring the best AI models across image generation, video production, audio, text, and computer vision.

Vast.AI's Model Library: Choosing from the Best AI Models Available


## Introduction

OpenAI has just released the GPT-OSS family of models, marking their return to open-weight model releases. The family includes GPT-OSS-120B (120 billion parameters) and GPT-OSS-20B (20 billion parameters), offering developers access to state-of-the-art language capabilities previously locked behind proprietary APIs.

GPT-OSS offers configurable reasoning capabilities with reasoning levels - from "low" for quick responses to "high" for complex problem-solving. The model uses OpenAI's harmony encoding system, which structures conversations and reasoning in a sophisticated format that enables fine-grained control over the model's thinking process.

This breakthrough means you can now:

-   Deploy OpenAI-quality models on your own infrastructure
-   Scale inference according to your exact needs

Vast.ai provides the perfect platform for running GPT-OSS models. With its marketplace of GPUs, you can choose the right hardware for your needs - whether running the efficient 20B model or the powerful 120B variant.

In this guide, we'll show you how to deploy both GPT-OSS models on Vast.ai using vLLM for optimized inference, with a focus on the 120B model. You'll learn how to interact with these models using the harmony encoding system for different reasoning levels.

## Setting Up the Environment

Before we can deploy our model, we need to set up our Vast.ai environment. First, install the Vast SDK:

```bash
# Install required packages
pip install --upgrade vastai
pip install --upgrade openai
pip install --upgrade openai-harmony
```

Set up your Vast API key (available from your [Account Page](https://cloud.vast.ai/account/)):

```bash
# Set your Vast.ai API key
export VAST_API_KEY="" # Your key here
vastai set api-key $VAST_API_KEY
```

## Choosing the Right Hardware

The GPT-OSS models have different hardware requirements:

-   **GPT-OSS-120B**: Requires an H100 GPU with 80GB VRAM
-   **GPT-OSS-20B**: Requires 16GB+ VRAM

For this guide, we'll demonstrate with the 120B model on H100. Let's search for suitable instances:

```bash
# Search for suitable GPU instances
vastai search offers " \
gpu_name = H100_SXM \
geolocation=US \
static_ip = true \
direct_port_count >= 1 \
verified = true \
disk_space >= 120 \
rentable = true"
```

## Deploying GPT-OSS with vLLM

Now let's deploy our GPT-OSS instance using vLLM's optimized inference server. We'll use the latest vLLM image with special GPT-OSS support. The pip install commands are from the model's Hugging Face pages ([120B](https://huggingface.co/openai/gpt-oss-120b), [20B](https://huggingface.co/openai/gpt-oss-20b)).

### Deploying GPT-OSS-120B (H100 recommended)

```bash
# Deploy vLLM instance for 120B model
export INSTANCE_ID= # Insert instance ID from search results

vastai create instance $INSTANCE_ID --image vllm/vllm-openai:latest --env '-p 8000:8000' --disk 160  --onstart-cmd 'uv pip install --system --upgrade transformers kernels torch openai; uv pip install --system --pre vllm==0.10.1+gptoss --extra-index-url https://wheels.vllm.ai/gpt-oss/ --extra-index-url https://download.pytorch.org/whl/nightly/cu128; vllm serve openai/gpt-oss-120b --max-model-len 80000'
```

### Alternative: Deploying GPT-OSS-20B on A100

Here's how to deploy the 20B model on an A100 GPU:

```bash
# Deploy vLLM instance for 20B model
export INSTANCE_ID= # Insert instance ID from search results

vastai create instance $INSTANCE_ID --image vllm/vllm-openai:latest --env '-p 8000:8000' --disk 80  --onstart-cmd 'uv pip install --system --upgrade transformers kernels torch openai; uv pip install --system --pre vllm==0.10.1+gptoss --extra-index-url https://wheels.vllm.ai/gpt-oss/ --extra-index-url https://download.pytorch.org/whl/nightly/cu128; vllm serve openai/gpt-oss-20b'
```

After deployment, wait for the model to download and start serving. You can monitor the logs to see when it's ready. Once running, find your instance's IP address and port from the [Instances tab](https://cloud.vast.ai/instances/) in the Vast AI Console.

## Interacting with GPT-OSS Using Harmony Encoding

GPT-OSS uses OpenAI's harmony encoding system, which provides structured conversation formatting and enables different reasoning levels. Let's set up our client:

```python
from openai import OpenAI
from openai_harmony import (
    load_harmony_encoding,
    HarmonyEncodingName,
    Role,
    Message,
    Conversation,
    SystemContent,
    DeveloperContent,
)

# Your server details
VAST_IP_ADDRESS = ""
VAST_PORT = ""

# Initialize client
client = OpenAI(
    api_key="EMPTY",
    base_url=f"http://{VAST_IP_ADDRESS}:{VAST_PORT}/v1"
)

# Load harmony encoding
enc = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)

## Simple Chat Function
def chat_gpt_oss(prompt, reasoning="medium"):
    """
    Simple chat with GPT-OSS
    reasoning: "low", "medium", or "high"
    """
    # Create conversation with harmony format
    conversation = Conversation.from_messages([
        Message.from_role_and_content(Role.SYSTEM, SystemContent.new()),
        Message.from_role_and_content(
            Role.DEVELOPER,
            DeveloperContent.new().with_instructions(f"Reasoning: {reasoning}")
        ),
        Message.from_role_and_content(Role.USER, prompt),
    ])

    # Get formatted prompt
    formatted_prompt = enc.render_conversation_for_completion(conversation, Role.ASSISTANT)

    # Make API call
    response = client.completions.create(
        model="openai/gpt-oss-120b",
        prompt=formatted_prompt,
        max_tokens=2000,
        temperature=0.7,
    )

    return response.choices[0].text


prompt = "Explain quantum computing to a 4th grader. Keep in concise."
```

## Comparing Reasoning Levels

One of GPT-OSS's most interesting features is its configurable reasoning levels. Let's see how the model responds differently to the same prompt with low and high reasoning:

### Low Reasoning Response

```python
result = chat_gpt_oss(prompt, "low")
print("----LOW REASONING----")
print(result)
```

Output:

```
----LOW REASONING----
analysisWe need to explain quantum computing to a 4th grader, concise. Use simple analogies, like bits as light switches, qubits as coins that can be heads, tails, or both. Keep short.assistantfinal**Quantum Computing in a Nutshell (for a 4th‑grader)**

1. **Regular computers = light switches**
   - A normal computer works with bits that are either **0** (off) or **1** (on), just like a light switch that's either down or up.

2. **Quantum computers = magic coins**
   - A quantum computer uses **qubits**.
   - Think of a qubit like a coin that can be **heads (0)**, **tails (1)**, **or both at the same time** while it's spinning in the air. That "both at once" is called **superposition**.

3. **Working together**
   - When you have many regular bits, you have to try every possible combination one after another.
   - With many qubits, they can be in many combinations **all at once** because they can be linked together in a special way called **entanglement**. It's like a group of magic coins that always know what the others are doing, even if they're far apart.

4. **Why it matters**
   - Because of superposition and entanglement, a quantum computer can solve some puzzles (like cracking codes or simulating molecules) **much faster** than a regular computer.

**Bottom line:** A regular computer is like a row of on/off switches; a quantum computer is like a bunch of spinning magic coins that can be many things at once, letting it try many solutions at the same time.
```

### High Reasoning Response

```python
result = chat_gpt_oss(prompt, "high")
print("----HIGH REASONING----")
print(result)
```

Output:

```
----HIGH REASONING----
analysisWe need to explain quantum computing to a 4th grader, concise. Use simple analogies, avoid jargon, but keep accurate. Should be short. Provide analogies like coins, dice, superposition, entanglement. Keep concise.assistantfinal**What is a quantum computer?**

Imagine you have a super‑smart robot that can solve puzzles much faster than a regular computer. A quantum computer is that robot, but instead of using normal "on/off" switches (bits) like a regular computer, it uses tiny, magical switches called **qubits**.

### Bits vs. Qubits
- **Bit (regular computer):** Like a tiny light that can be **off (0)** or **on (1)**.
- **Qubit (quantum computer):** Like a tiny spinning top that can be **0**, **1**, **or both at the same time** (this is called *superposition*).

### Why is that cool?
If you have 10 bits, they can show only one of 2¹⁰ = 1,024 possible patterns at a time.
If you have 10 qubits, they can be in all 2¹⁰ = 1,024 patterns **at once**! That lets a quantum computer try many solutions together.

### A Simple Analogy
Think of a maze puzzle:

- **Regular computer:** Sends a single mouse down the maze, step by step, until it finds the exit.
- **Quantum computer:** Sends a whole swarm of mice that can explore every possible path at the same time and tell you the exit almost instantly.

### Entanglement (Teamwork)
Sometimes two qubits become "best friends" and link together, no matter how far apart they are. Changing one instantly changes the other. This is called **entanglement**, and it lets quantum computers coordinate their work in a super‑efficient way.

### Bottom Line
A quantum computer uses qubits that can be 0, 1, or both at once, and they can be entangled with each other. This lets the machine look at many possibilities at the same time, solving certain problems way faster than ordinary computers.

So, it's like a super‑fast, super‑clever puzzle‑solver that can try lots of answers all at once!
```

## Comparing Low vs High Reasoning Outputs

The quantum computing explanations demonstrate the distinct capabilities of GPT-OSS-120B's reasoning levels. With low reasoning, the model produced a concise, structured explanation using simple analogies like "magic coins" and "light switches" - delivering clear concepts in a compact format suitable for quick understanding.

The high reasoning response revealed significantly more sophisticated thinking. The model provided detailed analogies like the maze puzzle with mice, explained mathematical concepts (2¹⁰ patterns), and structured the explanation with clear sections and formatting. Most notably, it offered multiple perspectives on the same concepts - from the spinning top analogy to the teamwork explanation of entanglement.

This difference in reasoning depth represents a significant advancement in model control. Rather than simply adjusting temperature or other parameters, GPT-OSS allows direct specification of how thoroughly the model should think through problems. The low reasoning approach prioritized efficiency while maintaining accuracy, while high reasoning prioritized comprehensive understanding with rich examples and detailed explanations.

## Conclusion

OpenAI's first open-source model thus introduces not just accessibility to advanced language capabilities, but a new paradigm for controlling model reasoning depth - enabling applications to dynamically adjust between quick responses and thorough analysis based on context and requirements.


Learn how to deploy and run OpenAI's GPT-OSS models on Vast.ai using vLLM, with configurable reasoning levels and harmony encoding system.

Posts about: Gpu Computing

Vast.AI's Model Library: Choosing from the Best AI Models Available

Running OpenAI's GPT-OSS on Vast.ai