Claude Code (Cloud Compute)

Installation

1.1 Install npm

sudo apt install npm

1.2 Install Claude Code

sudo npm i -g @anthropic-ai/claude-code@2.1.112

If access to the default npm registry is restricted, install from a mirror registry instead.

sudo npm i --registry=https://registry.npmmirror.com -g @anthropic-ai/claude-code@2.1.112

Note: Version 2.1.112 is the currently supported release. Do not install a later version.

Verify the installation:

claude --version

If a version number is returned, Claude Code has been installed successfully.

Note: On K1, install nvm and switch to the required Node.js version before using Claude Code.

wget -qO- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash
source ~/.bashrc
NVM_NODEJS_ORG_MIRROR=https://archive.spacemit.com/nodejs/k1 nvm install 22
nvm use 22

Configuration

2.1 Set Environment Variables

After obtaining the provider URL and API key, add them to ~/.bashrc as follows:

cat >> ~/.bashrc << 'EOF'
export ANTHROPIC_BASE_URL="Provider URL"
export ANTHROPIC_AUTH_TOKEN="Generated API key"
EOF
source ~/.bashrc

2.2 Configure Automatic API Key Approval

(cat ~/.claude.json 2>/dev/null || echo 'null') | jq --arg key "${ANTHROPIC_API_KEY: -20}" '(. // {}) | .customApiKeyResponses.approved |= ([.[]?, $key] | unique)' > ~/.claude.json.tmp && mv ~/.claude.json.tmp ~/.claude.json

Basic Usage

Start an interactive Claude Code session:

claude

The startup screen appears below.

Example greeting using the Say Hello prompt:

Use /model to switch models.

During an interactive session, Claude Code can help with:

• Answering programming questions
• Generating and optimizing code
• Debugging and refactoring existing code
• Providing technical recommendations and best practices

Enter /exit to end the session.

Example Workflow

This example demonstrates Claude Code generating and debugging an ONNX Runtime image-classification program.

• Enter the following request:

  Write a program that calls ONNX Runtime, downloads and uses the ResNet50 model for image classification, and displays the classification result.

  Claude Code starts analyzing the request and generating the program.

  

• Claude Code completes the implementation and provides the execution steps.

  

• Run the program. If an exception occurs, Claude Code analyzes the error and applies a fix.

  

• After the fix, the program runs successfully.

  

• The program can also be executed manually and produces the expected result.

  

Connecting to On-Device AI: Basic Trial

Claude Code can also connect to a local on-device inference service for basic experimentation. This section is optional and serves as a simple local trial.

5.1 Set Environment Variables

cat >> ~/.bashrc << 'EOF'
export ANTHROPIC_BASE_URL="http://localhost:8080"
export ANTHROPIC_AUTH_TOKEN="llama"
EOF
source ~/.bashrc

5.2 Configure Automatic API Key Approval

(cat ~/.claude.json 2>/dev/null || echo 'null') | jq --arg key "${ANTHROPIC_API_KEY: -20}" '(. // {}) | .customApiKeyResponses.approved |= ([.[]?, $key] | unique)' > ~/.claude.json.tmp && mv ~/.claude.json.tmp ~/.claude.json

5.3 Start llama-server

llama-server -m Qwen2.5-0.5B-Instruct-Q4_0.gguf -t 8 --host 127.0.0.1 --port 8080 --ctx-size 153600 --n-gpu-layers 0 --batch-size 512 --metrics --no-mmap

Note: Claude Code uses a long input context, so --ctx-size must be set high enough. This example uses 153600 because 15360 is not sufficient for this scenario.

5.4 Run Claude Code

Start Claude Code with the local model and run a simple greeting test.

claude --model qwen2.5:0.5b

The initial greeting may take longer because the model prefill can exceed 17,000 tokens.

Subsequent algorithm-generation tasks are noticeably faster, but output quality may still be constrained by the compact local model.