Deepseek V3 0324 is a state-of-the-art Mixture-of-Experts (MoE) model designed to redefine intelligent processing with 671B parameters. Released on March 24, 2025, it offers unmatched multilingual capabilities, particularly excelling in Chinese language processing. While local deployment offers full control, API access through Novita AI ensures cost-effectiveness, scalability, and enterprise-grade reliability.

What is Deepseek V3 0324?

Deepseek V3 0324 Benchmark

What is VRAM?

VRAM (Video Random Access Memory) is a specialized type of memory used by a computer’s GPU (Graphics Processing Unit). It stores and processes graphical data such as textures, 3D models, shaders, and framebuffers. VRAM is essential for rendering images, videos, and graphics in games, 3D modeling, video editing, and other visual applications.

What Does VRAM Mean for Accessing LLMs

VRAM Issues and Solutions for LLMs

Problem	Solution
📦Model Storage in VRAM Large models like GPT-4 require several GBs of VRAM to store weights, parameters, and computations. If the model size exceeds VRAM, it may not run efficiently or at all.	✔️ Use smaller or optimized models. ✔️ Offload parts of the model to system RAM or disk. ✔️ Use GPUs with higher VRAM or cloud services.
📊Batch Processing Batch size determines how many inputs can be processed simultaneously. Limited VRAM restricts batch size, reducing throughput and increasing latency.	✔️ Reduce batch size to fit VRAM limits. ✔️ Use multiple smaller batches. ✔️ Upgrade to GPUs with larger VRAM.
⚙️Model Optimization Limited VRAM may cause inefficiency or prevent the model from running entirely.	✔️ Use model quantization (e.g., 32-bit to 16-bit). ✔️ Offload computations to RAM or disk. ✔️ Use pruning to remove unnecessary parameters.
🧠Inference vs. Training Inference requires less VRAM (e.g., 8GB+ for large models), but training demands significantly more (e.g., 16GB–24GB+), depending on the model and dataset.	✔️ Use smaller or optimized models for inference. ✔️ Use cloud services or distributed training for large models. ✔️ Optimize data loading and storage strategies.

Deepseek V3 0324 VRAM Requirements

Pros:

• High Performance: The high VRAM and GPU requirements of Deepseek V3 0324 likely indicate its superior capabilities, enabling it to handle more complex tasks or run more precise models.
• Suitable for High-End Hardware: It can leverage the high-performance H100 GPUs, making it ideal for enterprise-level or research-level applications.

Cons:

• High Resource Consumption: The extremely high VRAM and GPU requirements significantly increase dependency on hardware resources, which can lead to higher operational costs.
• Limited Applicability: For individuals or small teams with limited resources, running Deepseek V3 0324 may not be feasible.
• Lack of Optimization: Compared to the 4-bit model, Deepseek V3 0324 appears to have lower resource utilization efficiency. Model optimization (e.g., quantization or pruning) is recommended.

Deploying Deepseek V3 0324 Locally VS API Access

Novita AI: A Reliable and Cost-Effective API Solution

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

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: 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 5: 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="<YOUR Novita AI API Key>",
)

model = "deepseek/deepseek-v3-0324"
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 V3 0324 combines cutting-edge technology with flexible deployment options, catering to diverse needs. Whether leveraging local control or cost-effective API access, this model empowers users with advanced function calling and multilingual capabilities. For seamless integration, Novita AI’s API provides an accessible entry point with minimal technical barriers.

Frequently Asked Questions

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing an affordable and reliable GPU cloud for building and scaling.

Recommend Reading

• Guide: Accessing Llama 4 Scout Locally, Through API, or on Cloud GPUs
• 10 Use Cases of Deepseek v3 0324
• Llama 4 Maverick on Novita AI Supports Function Calling