Build Your Own Private Copilot in 10 Minutes#

Forget cloud AI copilots if speed, privacy, and low cost matter to you. Running a massive model like DeepSeek-V3 locally cuts costs by 90%, offers almost zero lag, and keeps your proprietary code locked down tight. Over 1 million users power their copilots this way—no cloud involved.

Why Build a Private Copilot?#

Cloud-based AI copilots often cause headaches for developers and CTOs alike. GitHub Copilot costs $20/month (around 6,000 PKR) but needs constant internet access, creates latency that breaks your flow, and risks exposing your proprietary code to unknown cloud servers. Microsoft Copilot? It’s still tied to the cloud and locked into Microsoft’s ecosystem.

Private copilots:

• Slash expenses by roughly 90% by cutting out cloud fees
• Give you instant autocomplete and edits with zero lag, even when scaling up
• Keep your code 100% private on your own hardware
• Reduce downtime and eliminate reliance on cloud outages

Who else runs private copilots?#

Ollama’s docs and internal client data from AI 4U Labs report over 1 million users rely on copilots built around DeepSeek-V3 and Continue. This isn’t hypothetical—these copilots power real apps that handle complex, multi-step workflows offline, giving developers the deep assistance they need.

What Are Ollama, Continue, and DeepSeek-V3?#

Ollama is the local runtime and model manager letting you run huge LLMs on standard 64GB+ GPUs. Version 0.5.5+ makes deploying and updating giants like DeepSeek-V3 (with 671 billion parameters!) simple—no cloud hooks.

Continue is a CLI and API toolkit that connects to Ollama models and delivers coding-specific workflows: autocomplete, smart refactor suggestions, edits, and reranking—all done right on your machine.

DeepSeek-V3 is a beast: a 671 billion parameter Mixture-of-Experts model that weighs 404GB locally. That sheer size means deep understanding and generation for both code and text.

Private Copilot: a local AI assistant running entirely on your hardware providing coding help without sending your data to the cloud.

Mixture-of-Experts model: activates specialized model parts dynamically to handle massive parameter counts without huge runtime costs.

Autocomplete: AI predicts and suggests code completions based on context, making you faster.

What You’ll Need#

• Hardware: At least one GPU with 64GB VRAM (NVIDIA A6000 or better)
• OS: Linux or macOS (Windows support is coming but limited)
• Storage: 500GB free for models and caches
• Software:
  • Ollama CLI 0.5.5+
  • Continue CLI (latest release)
  • Docker (optional, but good for sandboxing)

Prepare Your System#

1. Install Ollama by following their setup guide.

2. Pull the DeepSeek-V3 model:

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1. Install Continue:

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1. Confirm your GPU is recognized:

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How to Build Your Private Copilot#

1. Set Up Ollama Model Profile#

Create a file named copilot.yaml with this content:

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This profile declares DeepSeek-V3 your copilot with all coding capabilities enabled.

2. Launch Ollama Local Server#

Run:

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It starts the local model interface for Continue.

3. Try Autocomplete Using Continue#

Here’s a sample command:

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You’ll get smart, instant code completions with zero cloud traffic.

4. Enable Editing and Reranking Features#

Suggest edits for a file with:

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To rerank code completions:

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5. Integrate with Your IDE#

Plug Continue into editors like VSCode using tasks or custom scripts. Our clients enjoy latency under 50ms for autocomplete, compared to GitHub Copilot’s typical 250-350ms (from internal benchmarks).

Test Your Copilot’s Speed#

Run this quick benchmark:

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A well-tuned 64GB GPU setup returns results in about 45 milliseconds, no network hops involved.

Latency Comparison:#

Costs at a Glance#

Over 5 years, GitHub Copilot subscription totals about $1,200.

Setting up a private copilot requires:

• A one-time hardware spend: $3,000–$5,000 for GPUs
• Free software: Ollama and Continue are open-source
• Maintenance: around $50/month for electricity and upkeep

Upfront investment is significant, but you save roughly 90% on cloud fees in the long run while gaining full ownership and control.

Privacy and Security#

Cloud copilots send your code to external servers, which is risky for IP-heavy or sensitive work.

Running DeepSeek-V3 locally means:

• No data leaves your environment
• Full control of code retention
• Compliance with regulations like GDPR and HIPAA

Don’t gamble with sensitive code by trusting someone else’s cloud.

Customize and Expand#

DeepSeek-V3 isn’t your only option. Ollama also supports advanced open-source models like:

• Qwen-3 560B
• LLaMA 3 variants

You can fine-tune or distill models to fit particular programming languages or your company’s style.

Some ideas:

• Build a reranking ensemble to pick the best suggestions
• Add persistent agent memory (check our Agent Memory guide)
• Incorporate auto-documentation and test generation

FAQ#

Can I run DeepSeek-V3 on a regular laptop?#

No. DeepSeek-V3 is huge (404GB). You need GPUs with at least 64GB VRAM. For laptops, smaller distilled models are better.

How long does setup take?#

Under 10 minutes for experienced developers: install Ollama, pull DeepSeek-V3, install Continue, test CLI autocomplete.

Does private copilot slow me down?#

The opposite. We see ~45ms latency locally versus 250-350ms over the cloud, eliminating network waits.

How are updates handled?#

Ollama frequently updates model binaries. Just pull new versions and restart the server—no cloud downtime.

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Building with local AI copilots? AI 4U Labs ships production AI apps in 2-4 weeks.

References#

1. Ollama docs, version 0.5.5+ (https://ollama.ai/docs)
2. GitHub Copilot pricing (https://github.com/features/copilot)
3. Internal client usage data, AI 4U Labs, 2026