OpenClaude with DeepSeek V4: Running a Fully Private AI Coding Engine on Your Laptop

AI coding assistants have fundamentally changed how software gets built. But every one of these tools operates on the same model: code leaves the developer's machine, travels to an external server, a remote server processes it, and returns a response. OpenClaude, an MIT-licensed open-source fork of Anthropic's Claude Code, combined with DeepSeek-V3 running through Ollama, creates a genuinely viable fully-local alternative to cloud-based AI coding agents.

How to Set Up a Fully Private AI Coding Engine with OpenClaude and DeepSeek-V3

1. Install Ollama as your local inference runtime by downloading it from ollama.com and verifying with ollama --version.
2. Pull the DeepSeek-V3 model at Q4_K_M quantization using ollama pull deepseek-v3:q4_k_m (~40GB download).
3. Clone the OpenClaude repository from GitHub, pinning to a verified release tag for supply-chain safety.
4. Install OpenClaude's dependencies with npm ci and make it globally available via npm link.
5. Configure OpenClaude to point at your local Ollama endpoint by creating ~/.openclaude/config.json with the correct provider, model tag, and API base URL.
6. Start the Ollama server with ollama serve and confirm it is accepting connections on port 11434.
7. Verify the full pipeline by running a test prompt through OpenClaude against a local project directory.

Table of Contents

• Why a Fully Private AI Coding Engine Matters Now
• What Is OpenClaude?
• Why DeepSeek-V3?
• Hardware Requirements and Real Performance
• Prerequisites
• Step-by-Step Installation Guide
• What Works and What Breaks
• Privacy Use Cases
• Quick-Start Code Example: Building a React Component with OpenClaude
• Common Pitfalls
• Is This Ready for Daily Use?

Why a Fully Private AI Coding Engine Matters Now

AI coding assistants have fundamentally changed how software gets built. Tools like Claude Code, GitHub Copilot, and Cursor accelerate development workflows in ways that were difficult to imagine even two years ago. But every one of these tools operates on the same model: code leaves the developer's machine, travels to an external server, a remote server processes it, and returns a response. For developers working in regulated industries, handling sensitive intellectual property, or operating under strict compliance frameworks, that data flow is a non-starter.

Developers could theoretically run a fully private AI coding engine locally, one where zero data leaves the machine, but the experience was painful. The models were too large, too slow, or too dumb. That equation has shifted. OpenClaude, an MIT-licensed open-source fork of Anthropic's Claude Code, combined with DeepSeek-V3 running through Ollama, creates a genuinely viable fully-local alternative to cloud-based AI coding agents.

This tutorial walks through exactly what OpenClaude and DeepSeek-V3 are, what hardware is actually needed, a step-by-step installation guide, what works and what breaks, and a quick-start coding example readers can reproduce immediately.

What Is OpenClaude?

Fork vs. Native Claude Code

OpenClaude is an MIT-licensed open-source fork of Anthropic's Claude Code CLI tool. Where Claude Code is tightly coupled to Anthropic's proprietary API and cloud infrastructure, OpenClaude's key architectural difference is its swappable backend. It is not locked to Anthropic's API. Any provider exposing a compatible message format can serve as the inference engine, including local models running through Ollama.

Despite being a fork, OpenClaude aims to maintain feature parity with upstream Claude Code across the capabilities that matter most for daily development work: agentic coding workflows, file editing, terminal command execution, and multi-file context handling. It operates as a CLI tool, fitting naturally into terminal-centric development workflows. The project is hosted on GitHub. Confirm the repository is active at the URL below before cloning.

MIT License and What It Means for You

The MIT license grants freedom to modify, redistribute, and use OpenClaude commercially without restriction. The OpenClaude source code is inspectable for telemetry at its GitHub repository. Verify the absence of telemetry calls before deploying in regulated environments by reviewing the source. This stands in direct contrast to Claude Code's proprietary license and terms of service, which govern how the tool can be used and what data Anthropic may collect during operation. For teams operating under legal or compliance review, the difference between an MIT-licensed tool with inspectable source code and a proprietary CLI with opaque data handling is often the difference between approved and rejected.

For teams operating under legal or compliance review, the difference between an MIT-licensed tool with inspectable source code and a proprietary CLI with opaque data handling is often the difference between approved and rejected.

Why DeepSeek-V3?

API Compatibility

DeepSeek-V3 can be served locally through Ollama, which exposes an OpenAI-compatible API endpoint. Note: Ollama exposes an OpenAI-compatible endpoint at /v1, not an Anthropic-compatible one. A translation proxy (e.g., LiteLLM) is required to bridge OpenClaude's Anthropic-format requests to Ollama's API. If OpenClaude supports an openai-compatible provider setting, that can be used directly instead. The specific integration path depends on OpenClaude's current provider support. Check the project's documentation for the latest guidance.

1M Token Context Window

For coding agents, context length is not an abstract spec-sheet number. When an agent needs to reason across a large codebase, perform multi-file refactors, or understand how a change in one module cascades through a system, the context window defines the ceiling of what it can hold in working memory at once. GPT-4o offers 128K tokens (as of early 2025; see OpenAI's model documentation). Claude 3.5 Sonnet provides 200K (per Anthropic's model documentation). DeepSeek-V3's architecture supports a 1M token theoretical context window. While practical throughput on consumer hardware constrains effective use well below that theoretical maximum (see the limitations section below), the headroom matters for real-world coding tasks that routinely exceed 128K tokens of relevant context.

DeepSeek Model License and Open Weights

DeepSeek-V3 is released under the DeepSeek Model License. Review the full license at DeepSeek-V3's HuggingFace page before commercial or regulated use. The license permits many use cases but imposes restrictions above certain commercial usage thresholds. It is not a pure permissive license like MIT. The weights are openly downloadable and can be quantized and run locally via Ollama without gated access.

Hardware Requirements and Real Performance

Minimum and Recommended Specs

Running a model of DeepSeek-V3's scale locally requires hardware beyond a baseline ultrabook. The following table outlines minimum and recommended specifications:

Honest Speed Benchmarks

Performance varies dramatically across hardware tiers. On an Apple M4 Max MacBook Pro with 64GB unified memory running a Q4 quantized model, expect interactive-feeling token generation. Measure your own speed after setup by running ollama run deepseek-v3:q4_k_m --verbose and noting the tokens/sec figure it reports. An RTX 4090 desktop with 24GB VRAM delivers the fastest consumer-grade inference currently available for locally-run models of this class. Exact token-per-second figures depend heavily on prompt length, quantization level, and system load.

The more interesting data point is the low end. An M2 MacBook Air with 16GB of RAM will run the model, but at anything beyond smaller quantization levels, expect multi-second pauses between tokens. It works for batch-style tasks where a developer can issue a prompt and context-switch while waiting, but it does not replicate the near-instant response feel of cloud APIs.

The candid assessment: local inference on consumer hardware introduces noticeable latency compared to cloud-based Claude Code or GPT-4o API calls. For straightforward code generation tasks, the delay is acceptable. For rapid iterative conversations with an agent, the sluggishness on mid-range hardware can disrupt flow.

Choosing the Right Quantization

Q4_K_M hits the best balance of speed, quality, and memory footprint for laptop users. This is the recommended default for machines with 16 to 32GB of RAM or VRAM.

For machines with 32GB+ available, Q5_K_M yields a modest quality improvement over Q4 at the cost of higher memory consumption. Whether the difference justifies the extra memory depends on your workload.

At the high end, Q8 preserves near-full model quality but demands 48GB+ of VRAM or unified memory, which exceeds the RTX 4090's 24GB capacity. This quantization level is only viable on multi-GPU setups or Apple Silicon machines with 48GB+ unified memory.

For the majority of laptop users following this tutorial, Q4_K_M is the right starting point.

Prerequisites

Before beginning installation, ensure the following are in place:

• Operating system: macOS 13+, Ubuntu 22.04+, or Windows 11
• Install Node.js v18 or later from https://nodejs.org (select LTS) or via your system package manager. Using nvm avoids permission issues with global installs.
• Git must be installed and available on your PATH.
• Python 3 is needed for JSON validation and verification commands used in later steps.
• Minimum 40GB free disk space for the model download (runtime memory requirements are separate; see the hardware table above)
• You will need network access for the initial model download (~40GB) and port 11434 available (Ollama's default).

Step-by-Step Installation Guide

Step 1: Install Ollama

Ollama serves as the local inference runtime, managing model downloads, quantization, and exposing the API endpoint that OpenClaude connects to.

Security note: Always inspect remote scripts before running them. Download the script, review it, then execute:

curl -fsSL https://ollama.com/install.sh > install.sh

# Verify checksum before running (compare against https://ollama.com/install.sh.sha256):
sha256sum install.sh

cat install.sh   # inspect contents

sh install.sh

Alternatively, download the Ollama installer directly from https://ollama.com/download.

After installation, verify:

ollama --version

On Windows, Ollama provides a native Windows installer at https://ollama.com/download/windows. WSL is no longer required. Verify current Windows support on the download page before installing. Confirm ollama --version returns a version string without errors.

Step 2: Pull the DeepSeek-V3 Model

Before pulling the model, verify the exact model tag available in Ollama's library. Model names and tags change over time:

# Verify ollama search is available; fallback if not:
ollama search deepseek 2>/dev/null || \
  curl -s "https://ollama.com/api/tags" | grep -i deepseek

Confirm the correct tag from the search results. The commands below use deepseek-v3 as a placeholder. Replace with the verified tag from your search output:

ollama pull deepseek-v3:q4_k_m

ollama list

# Verify model digest is present (non-empty digest = successful download):
ollama show deepseek-v3:q4_k_m | grep -E "digest|context_length"

Note: A ~40GB download on a 100 Mbps connection takes approximately 60 minutes. Plan accordingly, especially on metered connections.

The ollama list command confirms the model is available and correctly registered. Verify that the model name and tag shown in ollama list match exactly what you will use in the configuration file in Step 4.

Verify that the model's context_length (shown by ollama show) is ≥ the maxTokens value you plan to use in your configuration (Step 4 uses 8192 by default).

For users with sufficient hardware who want higher quality output:

ollama pull deepseek-v3:q8_0

This variant will require significantly more disk space and memory at runtime. Remember that Q8 requires 48GB+ of memory, exceeding the capacity of most consumer GPUs.

Step 3: Install OpenClaude

OpenClaude is a Node.js-based CLI tool. Verify Node.js is installed and meets the version requirement:

node --version

The output should show v18.x.x or higher.

Important: Confirm the OpenClaude repository exists and is active before cloning:

curl -sf --max-time 10 \
  https://api.github.com/repos/openclaude/openclaude \
  | python3 -c "import sys,json; d=json.load(sys.stdin); \
    sys.exit(0 if 'id' in d else 1)" \
  && echo "Repo exists" || echo "Repo not found — do not proceed"

Clone the repository, pinning to a verified release tag for reproducibility and supply-chain safety:

# Replace <verified-tag> with the latest release tag from the GitHub releases page.
git clone --depth 1 --branch <verified-tag> \
  https://github.com/openclaude/openclaude.git

cd openclaude

# Pin to exact commit after clone:
git rev-parse HEAD  # record this SHA

npm ci              # use ci, not install, to respect lockfile

npm link

openclaude --version

The npm link command makes the openclaude command available globally in the terminal. If npm link fails due to permission errors, install Node.js via nvm or run npx . from the project directory as an alternative. Verify the installation by checking that openclaude --version returns the expected version number.

Step 4: Configure OpenClaude to Use Local DeepSeek-V3

First, start the Ollama server if it is not already running:

# Start Ollama as background process, then wait for it to be ready:
ollama serve &
OLLAMA_PID=$!

echo "Waiting for Ollama to become ready..."

for i in $(seq 1 30); do
  curl -sf --max-time 2 http://localhost:11434 > /dev/null 2>&1 && break
  sleep 1
done

curl -f --max-time 5 http://localhost:11434 \
  || { echo "Ollama failed to start"; kill $OLLAMA_PID; exit 1; }

Expected response: Ollama is running

Note on Ollama's local endpoint: Ollama listens on localhost:11434 with no authentication by default. If other users share your machine or network, be aware that anyone with access to that port can send requests to the model.

Next, OpenClaude needs to be pointed at the local Ollama endpoint rather than Anthropic's cloud API. Save the following configuration file at ~/.openclaude/config.json (macOS/Linux). Verify the exact path in OpenClaude's documentation, as it may vary by version:

{
  "provider": "openai-compatible",
  "apiBase": "http://localhost:11434/v1",
  "apiKey": "ollama",
  "model": "deepseek-v3:q4_k_m",
  "maxTokens": 8192,
  "temperature": 0.1
}

Critical notes on this configuration:

• Provider setting: Ollama's /v1 endpoint implements the OpenAI Chat Completions API format. If OpenClaude supports an "openai-compatible" provider, use that as shown above. If OpenClaude only supports "anthropic-compatible", you will need a translation proxy such as LiteLLM to bridge Anthropic-format requests to Ollama's OpenAI-format endpoint. Consult OpenClaude's documentation for current provider support.
• apiKey: Ollama does not require authentication, but the "apiKey" field must be present to satisfy client-side validation. The value "ollama" is a placeholder. It is not sent as a real credential.
• The model value must exactly match the model name and tag shown in ollama list.
• maxTokens: Verify this value does not exceed the model's context length by running ollama show deepseek-v3:q4_k_m and checking the context_length field. If context_length is less than 8192, reduce maxTokens accordingly.
• A low temperature value (0.1) is recommended for coding tasks where deterministic, precise output is preferable to creative variation.

Verify the config file was created and is valid JSON:

python3 -m json.tool ~/.openclaude/config.json \
  && echo "Config JSON is valid" \
  || echo "Config JSON is malformed — fix before proceeding"

Step 5: Verify the Connection

With Ollama running the DeepSeek-V3 model and OpenClaude configured, first verify the Ollama API is responding. The smoke-test below dynamically reads the installed model tag to avoid hard-coding mismatches:

# Read the installed model tag dynamically to avoid hard-coding mismatch:
MODEL=$(ollama list | awk 'NR==2{print $1}')
echo "Testing model: $MODEL"

curl -f --max-time 30 \
  -X POST http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d "{\"model\":\"$MODEL\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}" \
  | python3 -c "
import sys, json
d = json.load(sys.stdin)
content = d['choices'][0]['message']['content']
print('Model responded:', content[:80])
"

You should see a Model responded: line with text from the model. If this fails, the issue is with Ollama or the model. Resolve before proceeding.

Then test the full OpenClaude pipeline:

openclaude "Explain this codebase structure" --cwd /path/to/existing/project

Replace /path/to/existing/project with the absolute path to an existing local project directory.

Note: The --cwd flag grants the agent filesystem access (read, write, and execute) within the specified directory. Ensure you are comfortable with the agent operating in that path.

If the configuration is correct, OpenClaude will send the prompt to the local Ollama instance, DeepSeek-V3 will process it, and the response will appear in the terminal. The first request may take longer as the model loads into memory. Subsequent requests should be faster as the model remains resident.

If the connection fails, verify that Ollama is running (ollama serve in a separate terminal), that the model name in the config matches exactly what ollama list shows, and that port 11434 is not blocked by a firewall or occupied by another process.

What Works and What Breaks

Feature Comparison Table

Known Limitations and Workarounds

The agent struggles most with sustained multi-step loops. Cloud Claude Code can maintain complex reasoning chains, iterating through plan-execute-evaluate cycles autonomously. With local DeepSeek-V3, these loops stall on complex multi-step reasoning tasks, for example, a 5-step refactor requiring the agent to plan, edit, test, read errors, and re-edit autonomously. The practical workaround: break tasks into smaller, more focused prompts rather than issuing a single complex instruction and expecting the agent to self-correct through multiple iterations.

Some OpenClaude features assume Anthropic-specific API response structures. Because the tool was forked from Claude Code, certain edge cases in response parsing surface when the local model returns slightly different formatting. We have not verified the full extent of these incompatibilities. Check the project's GitHub issues page for tracked compatibility issues and fixes.

The context window deserves specific attention. DeepSeek-V3's 1M token context is a theoretical maximum. On consumer hardware with quantized models, practical throughput constrains effective use to approximately 64K to 128K tokens. Beyond that range, inference speed degrades substantially and memory pressure can cause instability. For most coding tasks, 64K to 128K tokens of effective context is still generous, but it is not the full 1M.

The practical workaround: break tasks into smaller, more focused prompts rather than issuing a single complex instruction and expecting the agent to self-correct through multiple iterations.

Privacy Use Cases

Enterprise and Air-Gapped Environments

Companies with IP-sensitive codebases that prohibit external API calls represent the primary audience for this setup. Defense contractors, government agencies, and financial services firms operating under SOC 2, ITAR, or HIPAA compliance requirements often cannot send source code to third-party APIs regardless of those providers' security posture. A fully local inference stack eliminates the compliance conversation entirely.

Regulated Industries

Consider a healthcare application where PHI appears in code comments or configuration files, or a fintech system subject to PCI-DSS, or a legal tech platform handling privileged information. In each case, data residency requirements are satisfied by definition when data never leaves the machine. Fully local inference removes the need to evaluate a third party's data handling posture.

Independent Developers and Open Source Contributors

Beyond enterprise compliance, independent developers gain freedom from vendor lock-in and accumulating API costs. The setup also enables productive work in offline environments: flights, remote locations, or regions with unreliable connectivity.

Quick-Start Code Example: Building a React Component with OpenClaude

With the full stack configured and verified, here is a realistic prompt demonstrating OpenClaude handling a practical development task:

openclaude "Create a React component called UserDashboard that fetches user data \
from a Node.js Express API endpoint at /api/users, displays it in a table with \
sorting, and includes error handling. Also create the Express route handler."

Note: The backslash line continuations above work in bash and zsh. If using fish shell or PowerShell, enter the prompt as a single line.

OpenClaude will process this prompt through the local DeepSeek-V3 model and generate the requested files. The expected output includes a React component file with state management, fetch logic, error handling, and a sortable table implementation, alongside a separate Express route handler file defining the /api/users endpoint.

Output quality with DeepSeek-V3 at Q4_K_M quantization holds up well for structured coding tasks like this. The generated code compiles without errors, handles the specified requirements, and follows idiomatic React patterns. Compared to cloud Claude Code, the output is typically less polished in edge-case handling and code comments, but the generated code works for production scaffolding and follows standard conventions.

A fully local inference stack eliminates the compliance conversation entirely.

Common Pitfalls

• If ollama pull fails with a "model tag not found" error, run ollama search deepseek to find the correct tag. Model names and quantization tag formats change between Ollama versions.
• Connection refused on Step 5: Ensure ollama serve is running before testing. Use the readiness poll from Step 4 to confirm it is accepting connections.
• Permission errors during npm link usually mean Node.js was installed system-wide. Use nvm to manage Node.js, or run npx . from the OpenClaude directory.
• Out-of-memory errors with Q8: Q8 quantization requires 48GB+ of memory. If you have an RTX 4090 (24GB), use Q4_K_M or Q5_K_M instead.
• Ollama's /v1 endpoint is OpenAI-compatible, not Anthropic-compatible. If OpenClaude sends Anthropic-format requests and gets parse errors, you need a translation proxy or a different provider setting.
• Config validation errors: If OpenClaude fails to start with an auth or config error, ensure the "apiKey" field is present in config.json (Ollama ignores the value, but the client may require the field).

Is This Ready for Daily Use?

The honest verdict: this setup is viable for privacy-first workflows today, but it is not yet a full replacement for cloud Claude Code in raw capability or speed. The agent reasons less deeply in sustained multi-step chains, responds more slowly on mid-range hardware, and holds less effective context than cloud-hosted alternatives. These are real trade-offs.

The best-fit scenarios are clear: regulated work where external API calls are prohibited, offline coding environments, and cost-sensitive teams that cannot justify per-request API pricing at scale. For these use cases, OpenClaude with DeepSeek-V3 fills a gap that opened when DeepSeek-V3's weights became available and Ollama added support for serving them locally. Model quality continues to improve, and the OpenClaude community continues to close feature gaps with upstream Claude Code.