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Llama 3.2 1B, Qwen2.5 7B, Qwen 3 (0.6B, 1.7B, 4B) ,GLM 4 — all free available now on Novita AI to supercharge your projects without spending a dime!
Looking to harness the power of advanced AI without breaking the bank on hardware? DeepSeek R1 Distill Qwen 7B delivers 90% of the performance of the massive DeepSeek R1 671B model while drastically reducing hardware requirements. With a quantized version that runs on mid-range GPUs (as low as 4.5GB VRAM), this model empowers developers to tackle math reasoning, multilingual tasks, and more—efficiently and affordably.
This article will show you how DeepSeek R1 Distill Qwen 7B can help you!
What is DeepSeek R1 Distill Qwen 7B?
Benchmark of DeepSeek R1 Distill Qwen 7B
| Model | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
| GPT 4o 0513 | 9.3 | 13.4 | 74.6 | 49.9 | 32.9 | 759 |
| Claude 3.5 Sonnet 1022 | 16.0 | 26.7 | 78.3 | 65.0 | 38.9 | 717 |
| o1 mini | 63.6 | 80.0 | 90.0 | 60.0 | 53.8 | 1820 |
| QwQ 32B Preview | 44.0 | 60.0 | 90.6 | 54.5 | 41.9 | 1316 |
| DeepSeek R1 Distill Qwen 7B | 55.5 | 83.3 | 92.8 | 49.1 | 37.6 | 1189 |
DeepSeek R1 Distill Qwen 7B is a strong contender in mathematical reasoning tasks, showing competitive results in general performance.
However, it lags behind in general QA and programming benchmarks compared to top-performing models like o1 mini and QwQ 32B Preview.
Its exceptional mathematical performance is likely due to its base model being Qwen 2.5 Math, which is highly optimized for reasoning tasks.
DeepSeek R1 Distill Qwen 7B Hardware Requirements
| Model Type | Name | Size | Hardware Requirements |
|---|---|---|---|
| Full Model | DeepSeek-R1-Distill-Qwen-7B | ~18 GB | NVIDIA RTX 4090 (24GB VRAM) or higher |
| Quantized Model | DeepSeek-R1-Distill-Qwen-7B | ~4.5 GB | NVIDIA RTX 3060 (12GB VRAM) or higher |
By leveraging distillation, DeepSeek R1 Distill Qwen 7B significantly reduces hardware requirements while retaining over 90% of the original 671B model’s performance, particularly in mathematical reasoning and QA tasks. Its quantized model further enhances accessibility by enabling deployment on mid-range GPUs.
DeepSeek R1 Distill Qwen 7B VS Other Small Models
DeepSeek R1 Distill Qwen 7B VS Qwen 2.5 7B
| Category | DeepSeek R1 Distill Qwen 7B | Qwen-2.5-7B |
|---|---|---|
| Model Size | 7.62B | 7.61B |
| Open Source | Yes | Yes |
| Architecture | Transformer | Transformer |
| Language Support | 29+ languages | 29+ languages |
| Multimodal | Text-to-text only | Text-to-text only |
| Training Data | Fine-tuned on reasoning data | 18 trillion tokens |
| MATH (pass@1) | 92.8 | 49.8 |
| GPQA (pass@1) | 49.1 | 36.4 |
| VRAM (Full Model) | 18GB (RTX 4090 or higher) | 17.18GB (RTX 4090) |
DeepSeek-R1-Distill-Qwen-7B: Strong in math tasks, lower hardware requirements (quantized model available).
Qwen-2.5-7B: Balanced performance, excels in coding and multilingual tasks, trained on a larger dataset.
DeepSeek R1 Distill Qwen 7B VS Qwen 3 8B
| Category | DeepSeek R1 Distill Qwen 7B | Qwen 3 8B |
|---|---|---|
| Model Size | 7.62B | 8.19B |
| Open Source | Yes | Yes |
| Architecture | Transformer | Dense |
| Language Support | 29+ languages | 119 languages |
| Multimodal | Text-to-text only | Text-to-text only |
| MATH (pass@1) | 92.8 | 90.0 |
| GPQA (pass@1) | 49.1 | 59.0 |
| VRAM (Full Model) | 18 GB (RTX 4090) | 17.89 GB (RTX 4090) |
DeepSeek: Best for math tasks and lower GPU requirements (quantized model).
Qwen 3 8B: Better for multilingual tasks and long-context applications.
DeepSeek R1 Distill Qwen 7B VS Llama 3.1 8B
| Category | DeepSeek R1 Distill Qwen 7B | Llama 3.1 8B |
|---|---|---|
| Model Size | 7.62B | 8B |
| Open Source | Yes | Yes |
| Architecture | Transformer | Dense Transformer |
| Language Support | 29+ languages | 8 languages |
| Multimodal | Text-to-text | Text, Code (Input/Output) |
| Training Data | Fine-tuned on reasoning data | Pretrained on ~15T tokens, fine-tuned with 25M synthetic examples |
| MATH (pass@1) | 92.8 | 51.9 (CoT) |
| GPQA (pass@1) | 49.1 | 30.4 |
| VRAM (Full Model) | 18 GB (RTX 4090, FP16) | 17.17 GB (RTX 3090, FP16) |
DeepSeek-R1-Distill-Qwen-7B: Stronger in math and reasoning tasks .
Llama 3.1 8B: Excels in code generation and supports longer context (128,000 tokens), making it better for complex, long-context tasks.
How to Access Deepseek R1 Distilled Models?
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.
In addition to Deepseek R1 Distilled Models , Novita AI also provides free Qwen2.5 7B, Qwen 3 (0.6B, 1.7B, 4B) ,GLM 4 to support development of open source community!
Step 1: Log In and Access the Model Library
Log in to your account and click on the Model Library button.
Step 2: Choose Your Model and Start a Free Trail
Browse through the available options and select the model that suits your needs.
Step 3: Get Your API Key
To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.
Step 4: Install the API
Install API using the package manager specific to your programming language.
After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.
from openai import OpenAI
client = OpenAI(
base_url="https://api.novita.ai/v3/openai",
api_key="session_nkvtuVXXxS-LlR7txjZ3Rox8GhLMuv1R8IrIySNwTPN7xHJ0SVErFx3kNwJgkUEpcSM4F8c6zmcvyfuc1h59gw==",
)
model = "deepseek/deepseek-r1-distill-llama-8b"
stream = True # or False
max_tokens = 2048
system_content = ""Be a helpful assistant""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }
chat_completion_res = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": system_content,
},
{
"role": "user",
"content": "Hi there!",
}
],
stream=stream,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
response_format=response_format,
extra_body={
"top_k": top_k,
"repetition_penalty": repetition_penalty,
"min_p": min_p
}
)
if stream:
for chunk in chat_completion_res:
print(chunk.choices[0].delta.content or "", end="")
else:
print(chat_completion_res.choices[0].message.content)
DeepSeek-R1-Distill-Qwen-7B is a highly optimized model for mathematical reasoning tasks, achieving exceptional performance in benchmarks such as MATH . While it lags slightly behind in general QA and coding tasks compared to other top-performing models, its lower hardware requirements (with a quantized model option) make it accessible for a broader audience.
Frequently Asked Questions
DeepSeek-R1-Distill-Qwen-7B is a fine-tuned, distilled version of Qwen 2.5 Math optimized for mathematical reasoning and QA tasks. It supports multilingual text processing and offers a quantized model for deployment on mid-range GPUs.
Full Model: ~18 GB VRAM (NVIDIA RTX 4090 or higher).
Quantized Model: ~4.5 GB VRAM (NVIDIA RTX 3060 or higher).
Qwen3-Reranker-8B achieves top-tier scores:
MTEB-R: 69.02,
CMTEB-R: 77.45,
MTEB-Code: 81.22
It outperforms popular models like BGE and GTE in multiple categories.
Novita AI is the All-in-one cloud platform that empowers your AI ambitions. Integrated APIs, serverless, GPU Instance — the cost-effective tools you need. Eliminate infrastructure, start free, and make your AI vision a reality.
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