
The DeepSeek R1-0528 release introduces a significant improvement to the reasoning capabilities of language models by making it easier to access "thinking" mode without requiring complex prompt engineering or pre-pending thinking tokens. This enhanced model provides transparent, step-by-step reasoning that's particularly valuable for educational applications, complex problem solving, and scenarios where transparency in AI decision-making is crucial.

In this post, we'll explore how to deploy the **DeepSeek-R1-0528-Qwen3-8B** model using **vLLM** on Vast.ai's cloud GPU platform, leveraging the new **qwen3 reasoning parser** that simplifies access to the model's internal thinking process.

---

## The Open Source AI Revolution

DeepSeek R1-0528 represents more than just a technical advancement—it's a significant step toward democratizing advanced AI capabilities. As an **open source model**, it makes these reasoning and advanced AI capabilities more accessible.

Unlike closed-source models that require vendor dependence, usage costs, and data privacy concerns, DeepSeek R1-0528 enables **complete control** over your AI infrastructure. Organizations can now deploy advanced reasoning capabilities locally, maintaining data sovereignty while avoiding variable per-token pricing.

This democratization means organizations can access cutting-edge AI without massive infrastructure investments. Advanced reasoning capabilities are no longer exclusive to large tech companies—anyone with GPU access can deploy their own reasoning-capable AI system.

---

## What Makes DeepSeek R1-0528 Special?

The DeepSeek R1-0528 release addresses a key limitation in previous DeepSeek models: the complexity of accessing the model's internal reasoning process. Previously, users needed to manually prepend "thinking" tokens to model outputs or use complex prompt engineering to see step-by-step reasoning.

---

## Deploying DeepSeek R1-0528 on Vast.ai

### Step 1: Set Up Vast.ai Environment

First, install the Vast CLI and configure your API key:

```bash
pip install --upgrade vastai

export VAST_API_KEY="your_vast_api_key"
vastai set api-key $VAST_API_KEY
```

### Step 2: Find Suitable Hardware

The DeepSeek-R1-0528-Qwen3-8B model requires specific hardware capabilities:

-   **Minimum 24GB GPU RAM** for model weights and KV cache
-   **Single GPU configuration** (sufficient for the 8B parameter model)
-   **Static IP address** for stable API endpoint hosting
-   **Direct port access** for the vLLM OpenAI-compatible server
-   **60GB+ disk space** for model storage and dependencies

Search for suitable instances:

```bash
vastai search offers "compute_cap >= 750 \
gpu_ram >= 24 \
num_gpus = 1 \
static_ip = true \
direct_port_count >= 1 \
verified = true \
disk_space >= 60 \
rentable = true"
```

### Step 3: Deploy the Model

Deploy using the vLLM OpenAI-compatible server with the qwen3 reasoning parser:

```bash
export INSTANCE_ID="your_instance_id"

vastai create instance $INSTANCE_ID \
--image vllm/vllm-openai:latest \
--env '-p 8000:8000' \
--disk 60 \
--args --model deepseek-ai/DeepSeek-R1-0528-Qwen3-8B \
--max-model-len 4096 \
--reasoning-parser qwen3
```

**Key deployment parameters:**

-   `--reasoning-parser qwen3`: Enables the simplified reasoning parser
-   `--max-model-len 4096`: Accommodates longer reasoning sequences
-   `--tensor-parallel-size 1`: Single GPU configuration
-   `--gpu-memory-utilization 0.90`: Optimized memory usage

### Step 4: Get Instance Details and Test Connection

Navigate to the [Instances tab](https://cloud.vast.ai/instances/) in the Vast AI Console and locate your instance. Click the IP address button to view the forwarded ports.

Test the connection with a simple curl command:

```bash
export VAST_IP_ADDRESS="your_ip_address"
export VAST_PORT="your_port"

curl -X POST http://$VAST_IP_ADDRESS:$VAST_PORT/v1/completions \
-H "Content-Type: application/json" \
-d '{"model": "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "prompt": "Hello, how are you?", "max_tokens": 50}'
```

---

## Testing the Model's Reasoning Capabilities

### Setup Python Client

```python
import time
from openai import OpenAI

# Replace with your actual Vast.ai instance details
VAST_IP_ADDRESS = "your_ip_address"
VAST_PORT = "your_port"

client = OpenAI(
    api_key="DUMMY",
    base_url=f"http://{VAST_IP_ADDRESS}:{VAST_PORT}/v1"
)
```

### Basic Functionality Test

```python
start = time.time()
test_resp = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
    messages=[{"role": "user", "content": "Hello"}]
)
elapsed = time.time() - start

print("Response:", test_resp.choices[0].message.content)
print(f"Elapsed time: {elapsed:.4f} s")
```

### Reasoning Capability Test

This is where DeepSeek R1-0528 truly shines. The model naturally provides step-by-step reasoning without complex prompt engineering:

```python
reasoning_prompt = """
Write the History of Paris in a paragraph, show your thinking
step by step first before paragraph and then write the history
in a paragraph
"""

start = time.time()
resp = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
    messages=[{"role": "user", "content": reasoning_prompt}]
)
elapsed = time.time() - start

print("Model output:\n", resp.choices[0].message.content)
print(f"Elapsed time: {elapsed:.3f} s")
```

**Expected output structure:**

1. **Thinking process**: The model shows its step-by-step reasoning
2. **Final answer**: A well-structured paragraph about Paris's history

### Mathematical Reasoning Test

```python
math_prompt = "If 8 × 7 = ?, show your work step by step."

resp = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
    messages=[{"role": "user", "content": math_prompt}]
)

print("Mathematical reasoning:\n", resp.choices[0].message.content)
```

The model will naturally break down the problem, show intermediate steps, and arrive at the solution with clear reasoning traces.

---

## Key Benefits of DeepSeek R1-0528

### 1. **Simplified Reasoning Access**

No need to prepend thinking tokens or use complex prompt engineering. The `qwen3` reasoning parser automatically handles the extraction and presentation of reasoning steps.

### 2. **Educational Value**

Perfect for educational applications where students need to understand the problem-solving process, not just the final answer.

### 3. **Transparency and Trust**

Users can see exactly how the model arrives at its conclusions, building trust in AI-powered decision-making systems.

### 4. **Cost-Effective Deployment**

Running on Vast.ai provides significant cost savings compared to major cloud providers while maintaining high performance.

### 5. **Easy Integration**

OpenAI-compatible API ensures seamless integration with existing applications and workflows.

---

## Use Cases

### Educational Applications

-   Step-by-step problem solving for mathematics, science, and logic
-   Teaching critical thinking and reasoning skills
-   Creating interactive learning experiences

### Business Intelligence

-   Transparent decision-making in automated systems
-   Audit trails for AI-powered recommendations
-   Complex analysis with clear reasoning paths

### Research and Development

-   Understanding model behavior and capabilities
-   Testing reasoning patterns across different domains
-   Developing more interpretable AI systems

---

## Conclusion

The DeepSeek R1-0528 release represents a significant step forward in making AI reasoning more accessible and transparent. By combining this powerful model with Vast.ai's cost-effective GPU infrastructure and the simplified qwen3 reasoning parser, developers can now easily deploy transparent, reasoning-capable AI systems.

Whether you're building educational tools, business intelligence systems, or research applications, DeepSeek R1-0528 on Vast.ai provides an excellent foundation for applications that require clear, step-by-step reasoning capabilities.

The elimination of complex prompt engineering for accessing reasoning modes makes this model particularly valuable for production applications where transparency and interpretability are key requirements.

---

## Useful Resources

-   [DeepSeek R1-0528 Model on Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B)
-   [vLLM Documentation](https://docs.vllm.ai/)
-   [Vast.ai GPU Marketplace](https://vast.ai)
-   [OpenAI Python SDK](https://github.com/openai/openai-python)

---

_Ready to deploy transparent, reasoning-capable AI? Get started with DeepSeek R1-0528 on Vast.ai today! 🚀_


Deploy DeepSeek R1-0528 on Vast.ai to access transparent, step-by-step AI reasoning without prompt tricks—ideal for education, research, and business use.

DeepSeek R1-0528: Enhanced Reasoning with Simplified Thinking Mode on Vast.ai


# DeepSeek R1: Open-Source Disruptor or Overhyped Upstart?

DeepSeek, a Chinese AI firm, has drawn attention in recent weeks for its release of DeepSeek R1 – a large language model (LLM) that aims to stand shoulder to shoulder with more established players. Its rapid rise has shaken up the AI race, with comparisons to OpenAI's o1 in particular.

## Try it For Yourself on Vast

Our [Ollama + WebUI](https://docs.vast.ai/ollama-webui) guide uses DeepSeek r1:70b as an example. Follow it to try out DeepSeek on our GPU cloud.

## The Disruptive Growth of DeepSeek

DeepSeek's impact isn't limited to industry insiders. It's already gaining massive popularity among everyday users. The DeepSeek mobile app, which provides a chatbot interface for DeepSeek R1, shot to the top of the Apple App Store charts within days of its release on January 20, 2025.

But the biggest reason DeepSeek is [making waves](https://techcrunch.com/2025/01/28/deepseek-everything-you-need-to-know-about-the-ai-chatbot-app/)? **The company has seemingly disrupted the prevailing wisdom that developing cutting-edge LLMs costs a ton of money.** The DeepSeek R1 LLM was developed at a fraction of the cost of comparable models from other companies – reportedly less than $6 million as opposed to hundreds of millions or even billions of dollars.

Another key factor setting it apart: **DeepSeek R1 is free and open source.** Researchers and developers can freely experiment with, modify, and deploy the model without the restrictions of proprietary alternatives.

The meteoric rise of the R1 LLM threw the U.S. financial markets into turmoil, prompting investors to rethink the valuation of companies like NVIDIA and reevaluate the AI prospects of tech giants like Microsoft and Meta. A significant stock market sell-off occurred on January 27, 2025.

Beyond its financial impact, though, what exactly is DeepSeek and how did it develop a model that's turning heads? Let's back up and take a closer look at this new player on the AI scene.

## What Is DeepSeek?

Based in Hangzhou, China, **DeepSeek is an AI development firm that focuses on creating open-source LLMs.** While it operates as an independent lab, DeepSeek is backed by High-Flyer Capital Management, a China-based quantitative hedge fund specializing in AI-driven financial strategies.

From the start, DeepSeek built its own data center clusters to train its generative AI models. U.S. export restrictions on advanced chips had an impact on operations, however. DeepSeek was forced to train its more recent models using NVIDIA H800 chips, a less powerful alternative to the H100 chips available to U.S. companies.

DeepSeek's first model was released in November 2023, and since then the firm has developed several different variations. But DeepSeek only catapulted to global prominence in January 2025, when its R1 model was released – along with a web interface, mobile application, and API access.

## DeepSeek R1: A Challenge to the Status Quo?

The DeepSeek R1 model focuses on logical inference, mathematical reasoning, and real-time problem-solving. Compared to other LLMs, **it uses a more efficient and sparse Mixture-of-Experts (MoE) architecture, where different parts of the model specialize in handling different tasks.** For every input, only a few "expert" neural networks are selected to process each piece of data.

What's really sparked buzz, however, is DeepSeek R1's ability to match performance with OpenAI's latest ChatGPT models across various tasks. So R1 is a more efficient model, developed much more cheaply, with impressive performance. It sounds almost too good to be true...

How was it trained?

According to DeepSeek, the R1 model leveraged the following training innovations:

-   **Reinforcement Learning** – Using a large-scale reinforcement learning approach without supervised fine-tuning, DeepSeek employed techniques like group relative policy optimization (GRPO) to enhance reasoning capabilities.
-   **Reward Engineering** – Instead of relying on traditional neural reward models, DeepSeek created a rule-based reward system that delivers superior results (i.e., by structuring incentives to guide the model's learning during training).
-   **Emergent Behavior Network** – DeepSeek discovered that advanced reasoning patterns can emerge on their own through reinforcement learning, without being explicitly programmed.
-   **Distillation** – DeepSeek used efficient knowledge transfer techniques and distilled knowledge from larger, more complex AI models, allowing R1 to achieve strong performance with reduced computational demands.

Not all of this is new. DeepSeek does present some algorithmic and architectural novelties – such as the sparse MoE design and the use of reinforcement learning to refine synthetic data selection and optimize reasoning pathways during training – but the company isn't necessarily the first to implement these techniques.

Still, it has combined them in a way that makes its R1 model notably efficient and competitive.

## Too Good to Be True?

Unfortunately, there is a catch. **DeepSeek R1 reportedly [relied](https://www.theverge.com/news/601195/openai-evidence-deepseek-distillation-ai-data) on synthetic data from OpenAI's GPT-4o for its training**, pushing some of the computational burden onto the latter, much less efficient model.

So while R1 is touted as being highly efficient, those claims only apply to the model itself, not to the entire process used to train it. The R1 model's lean MoE architecture means it is indeed efficient when running inference, but its training pipeline requires external synthetic data generated by other models like GPT-4o – and that external data generation is computationally expensive.

This means that a significant part of the total AI development cost and compute burden is still being offloaded onto other, more complex models. **It's a cost-saving move for DeepSeek, but it doesn't negate the need for massive specialized compute in the industry overall.**

Time will tell how sustainable DeepSeek's approach is – and what consequences it may have for the company as scrutiny over training methods increases and competition in the AI space continues to grow.

...There may even be national security concerns, as the U.S. government [eyes](https://www.cbsnews.com/news/deepseek-ai-raises-national-security-concerns-trump/) the potential for harmful foreign influence. But that's a topic for another day.

## The Bottom Line on DeepSeek R1

Regardless of how one feels about it, DeepSeek R1 has certainly made a bold entrance into the world. While its efficiency claims come with important caveats, the model remains a fascinating case study in how AI development is evolving.

Plus, its open-source license allows for commercial use, which is a pretty major draw for developers around the world.

## Want to Learn More

DeepSeek R1 is a **massive** model that will not fit on a single 8xH200 instance unless you run a quantized version. The 4bit quantization will run on a 8xH100 or 4xH200 node. The sweet spot might be the 8bit quantized model. If you are interested in trying it out, [reach out to us](https://vast.ai/contact-sales) so we can talk to you about your use case and help get it setup.

As always, we'd be happy to assist you in any way we can! We're here to support your AI experiments, providing the compute power you need to explore, test, and innovate – every day.


DeepSeek R1: Open-source LLM shaking up the AI race. Developed for under $6M, it rivals ChatGPT's performance. Try it on Vast's GPU cloud.

Posts about: Opensource

DeepSeek R1-0528: Enhanced Reasoning with Simplified Thinking Mode on Vast.ai

DeepSeek R1: Open-Source Disruptor or Overhyped Upstart?