---
title: DeepSeek R1 Distill Qwen 1.5B — Reasoning LLM | ModelsLab
description: Run powerful reasoning on laptop GPUs. DeepSeek R1 Distill Qwen 1.5B delivers math and code analysis in 4GB VRAM. Try the API.
url: https://modelslab.com/deepseek-r1-distill-qwen-15b
canonical: https://modelslab.com/deepseek-r1-distill-qwen-15b
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:08:53.464686Z
---

Available now on ModelsLab · Language Model

DeepSeek R1 Distill Qwen 1.5B
Reasoning. Laptop-sized.
---

[Try DeepSeek R1 Distill Qwen 1.5B](/models/deepseek/deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B) [API Documentation](https://docs.modelslab.com)

Compact Power. Serious Reasoning.
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Distilled Intelligence

### 671B Reasoning Compressed

Knowledge distilled from massive DeepSeek-R1 into 1.5B parameters without performance loss.

Hardware Efficient

### 4GB GPU Memory

Runs on single laptop GPU in 8-bit quantization for local deployment and edge inference.

Chain-of-Thought

### Math and Code Mastery

Excels at step-by-step problem solving, mathematical reasoning, and code comprehension tasks.

Examples

See what DeepSeek R1 Distill Qwen 1.5B can create
---

Copy any prompt below and try it yourself in the [playground](/models/deepseek/deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B).

Calculus Problem

“Solve this step-by-step: Find the derivative of f(x) = 3x^4 - 2x^2 + 5x - 1 and evaluate at x = 2. Show all work.”

Algorithm Analysis

“Explain the time complexity of a binary search tree insertion operation. Compare it to a linear search approach with code examples.”

Logic Puzzle

“Five people sit in a row. Alice is not next to Bob. Charlie sits between Diana and Eve. Who sits where? Work through the constraints.”

Code Debugging

“Debug this Python function that should return the sum of even numbers in a list: def sum\_evens(nums): total = 0; for n in nums: if n % 2 == 0: total += n; return total. Identify issues.”

For Developers

A few lines of code.
Reasoning. Four gigabytes.
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about DeepSeek R1 Distill Qwen 1.5B
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[Read the docs ](https://docs.modelslab.com)

### What is DeepSeek R1 Distill Qwen 1.5B and how does it work?

### Can I run DeepSeek R1 Distill Qwen 1.5B locally?

### What are the primary use cases for this DeepSeek R1 Distill Qwen 1.5B API?

### What quantization formats are available?

### Is fine-tuning supported for DeepSeek R1 Distill Qwen 1.5B?

Ready to create?
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Start generating with DeepSeek R1 Distill Qwen 1.5B on ModelsLab.

[Try DeepSeek R1 Distill Qwen 1.5B](/models/deepseek/deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-04-15*