AI/ML

How to Install Kimi K2 on Paperspace Gradient (DigitalOcean) – Full Setup Guide

Why Use Paperspace Gradient?

Paperspace Gradient is a powerful platform by DigitalOcean that allows you to:

Access high-end GPUs (A100, RTX 4090, etc.)
Launch notebooks or containers with minimal setup
Easily install and run open-source LLMs like Kimi K2
Benefit from JupyterLab, volume persistence and SSH access

Prerequisites

Paperspace account: https://www.paperspace.com
Hugging Face account (accept Kimi K2 license)
Basic Python familiarity (Jupyter or CLI)

Step 1: Log In and Create a Notebook

1. Go to https://www.paperspace.com/gradient

2. Click on “Create Notebook”

3. Select Base Container:

Recommended: PyTorch + CUDA 12 or Transformers + GPU

4. Choose machine type:

Use A100, 3090, or RTX 5000

5. Name your notebook (e.g., Kimi-K2-LLM) and click “Create Notebook”

Step 2: Update and Install Dependencies

Open the Jupyter notebook terminal and run:

sudo apt update && sudo apt install -y git-lfs
pip install torch torchvision transformers accelerate huggingface_hub

Step 3: Clone Kimi K2 Model

git lfs install
git clone https://huggingface.co/moonshotai/Kimi-K2-Instruct

cd Kimi-K2-Instruct

Make sure to accept the model license at: https://huggingface.co/moonshotai/Kimi-K2-Instruct

Step 4: Load Kimi K2 Model (Notebook Cell)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "moonshotai/Kimi-K2-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16).cuda()
prompt = "Explain generative AI to a 10-year-old."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Step 5 (Optional): Use CLI Instead of Notebook

1. Switch to terminal

2. Create and run a Python script (e.g., app.py)

3. Launch inference from the command line

Bonus: Save Your Workspace

Enable Persistent Storage to retain:

Model weights
Logs
Generated outputs
Python scripts

Final Thoughts

Paperspace Gradient is one of the easiest ways to run Kimi K2 in a notebook interface with GPU support. Whether you’re building AI applications, fine-tuning models, or testing prompts this setup is ideal for developers, researchers and solo builders.

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