In the rapidly evolving landscape of artificial intelligence, access to powerful AI models has traditionally been limited by significant cost barriers and technical requirements. DeepSeek AI, a cutting-edge language model developed by the Chinese AI company DeepSeek, has emerged as a game-changer in this space. With performance comparable to proprietary models like OpenAI's offerings, DeepSeek's open-source approach has democratized access to advanced AI capabilities. This comprehensive guide will walk you through various methods to use DeepSeek AI completely free, empowering you to leverage this technology without financial constraints.
DeepSeek AI is a collection of large language models (LLMs) that has gained significant attention in the AI community for its impressive capabilities in reasoning, mathematics, coding, and general knowledge tasks. What sets DeepSeek apart is not just its performance but its accessibility—the company has made several versions of its models available as open-source offerings, allowing researchers, developers, and enthusiasts to use them without the prohibitive costs associated with many commercial AI services.
The DeepSeek model family includes:
These models have shown remarkable performance in benchmarks, with some versions approaching or matching the capabilities of much more expensive proprietary models from major AI companies.
Before diving into more technical approaches, let's explore the easiest way to access DeepSeek models without any setup or technical knowledge: Anakin AI.
Anakin AI is an all-in-one AI platform that provides free access to a wide range of powerful AI models, including DeepSeek. This user-friendly platform allows you to instantly start using DeepSeek's capabilities without downloading anything or setting up complex environments.
Anakin AI offers several advantages for accessing DeepSeek:
With Anakin AI, you can experience DeepSeek's capabilities for tasks like answering questions, content generation, coding assistance, and problem-solving without worrying about hardware requirements or technical configurations.
For those who prefer running AI models locally on their own hardware for increased privacy, control, and potentially higher performance, Ollama provides an excellent solution for deploying DeepSeek models on your own computer.
The hardware requirements vary depending on which version of DeepSeek you plan to use:
Install Ollama:
curl -fsSL <https://ollama.com/install.sh> | shDownload and Run DeepSeek: Choose the appropriate model size based on your hardware capabilities:
# For entry-level systems (1.5B version)
ollama run deepseek-r1:1.5b
# For mid-range systems (7B version)
ollama run deepseek-r1:7b
# For better systems (8B Llama-based version)
ollama run deepseek-r1:8b
# For high-performance systems (14B version)
ollama run deepseek-r1:14b
Interacting with the Model: Once the model is loaded, you'll see a command prompt where you can start asking questions or giving prompts:
>>> What is the significance of quantum computing?
Managing Your Models:
ollama listollama rm deepseek-r1:7bollama show deepseek-r1While Ollama provides a command-line interface by default, you can enhance your experience with a graphical user interface. Popular options include:
To install Open WebUI, for example, you can use Docker:
docker run -d --name openwebui -p 3000:8080 -v open-webui:/app/backend/data --restart always ghcr.io/open-webui/open-webui:latest
Then access it at http://localhost:3000 in your browser.
If you don't have the necessary hardware to run DeepSeek locally but still want direct control over the model, several cloud platforms offer free or low-cost options for deploying DeepSeek models:
Create a new Google Colab notebook
Install the necessary packages:
!pip install transformers torch accelerate
Load and use DeepSeek:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "deepseek-ai/deepseek-r1-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Generate text
inputs = tokenizer("Write a poem about artificial intelligence", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
One of the most powerful applications of DeepSeek is building Retrieval-Augmented Generation (RAG) systems that can reference specific knowledge bases. Here's a simplified approach to creating a free RAG system with DeepSeek:
Install the necessary packages:
pip install langchain langchain-community langchain-deepseek-official
Set up document processing:
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Load document
loader = PyPDFLoader("your_document.pdf")
data = loader.load()
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
documents = text_splitter.split_documents(data)
Create embeddings and set up vector storage:
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
# Use free embedding model
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
# Create vector store
vectorstore = FAISS.from_documents(documents, embeddings)
Set up DeepSeek with the free NVIDIA NIM service:
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from langchain.chains import RetrievalQA
# Get free API key from NVIDIA NIM
llm = ChatNVIDIA(
model="deepseek-ai/deepseek-r1",
api_key="your_free_nvidia_nim_api_key",
temperature=0.7
)
# Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever()
)
# Query the system
result = qa_chain.invoke({"query": "What are the key points in the document?"})
print(result["result"])
If you prefer using DeepSeek through API calls without setting up the model yourself, several options provide free or low-cost access:
To get started with NVIDIA NIM:
The open-source community has developed numerous free tools to enhance your experience with DeepSeek:
If you're running DeepSeek locally on modest hardware, these optimization techniques can help:
DeepSeek represents a significant milestone in democratizing access to powerful AI models. By making advanced language models available as open-source offerings, DeepSeek has enabled individuals, researchers, and organizations to leverage state-of-the-art AI capabilities without the financial barriers that have traditionally limited access.
While running these models locally provides the most control and privacy, platforms like Anakin AI offer the most user-friendly approach to experiencing DeepSeek's capabilities immediately. Anakin's intuitive interface, combined with its free tier and access to multiple models beyond just DeepSeek, makes it an excellent starting point for anyone looking to explore the potential of this technology.
Whether you choose to use Anakin AI for its simplicity, run DeepSeek locally with Ollama for maximum control, or leverage cloud platforms for scalability, the democratization of AI technology through open-source models like DeepSeek is empowering a new generation of innovation and creativity.
As you begin your journey with DeepSeek, remember that the most powerful AI applications often come from combining these models with your unique data, problems, and insights. The tools and methods outlined in this guide provide just the starting point—the real magic comes from what you build with them.
