Developers working in secure environments often face a frustrating limitation: cloud-based AI coding assistants like GitHub Copilot, Cursor, and Claude Code require internet connectivity to function. For those in air-gapped networks—whether in government, healthcare, finance, or defense sectors—this creates a significant productivity gap. Fortunately, several alternatives let you run AI-powered code assistance entirely offline.

Why Local LLMs Matter for Air-Gapped Development

Cloud AI tools send your code to external servers for processing. This violates security policies in many organizations. Local LLMs run entirely within your infrastructure, ensuring sensitive code never leaves your network. Beyond compliance, local models offer predictable latency, unlimited usage without subscription costs, and full control over model selection.

The trade-off involves hardware requirements and setup complexity. Modern local models require decent GPU hardware or CPU-only inference with patience. However, the gap between cloud and local capability has narrowed considerably.

Comparing Local LLM Stacks for Coding in 2026

Before diving into individual tools, here is a head-to-head comparison of the leading local LLM stacks so you can pick the right foundation for your air-gapped environment:

For individual developers on air-gapped workstations, Ollama + Continue.dev wins on ease. For team deployments on internal servers, vLLM running DeepSeek-Coder-33B delivers near-GPT-4 quality with full local control.

Top Local LLM Options for Coding

Ollama: The Easiest Entry Point

Ollama has become the go-to solution for running local LLMs. It supports macOS, Linux, and Windows, with a simple command-line interface.

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a coding-optimized model
ollama pull codellama

# Run with specific parameters
ollama run codellama --temperature 0.2 --top-p 0.9

Ollama works well for code completion and explanation but lacks the sophisticated IDE integration of cloud tools. You can pair it with Continue.dev for VSCode or Zed for a more integrated experience.

Continue.dev: Local IDE Integration

Continue.dev provides IDE extensions that connect to local models. It supports Ollama, LM Studio, and other backends.

// Continue.dev config in .continue/config.json
{
  "models": [
    {
      "provider": "ollama",
      "model": "codellama:7b"
    }
  ],
  "tabAutocompleteModel": {
    "provider": "ollama",
    "model": "starcoder"
  }
}

This configuration enables inline autocomplete and chat functionality within VSCode or JetBrains IDEs, running entirely on local hardware.

LM Studio: Full-Featured Local AI

LM Studio offers a polished GUI for running various open-source models. It includes model discovery, fine-tuning options, and API endpoints that mimic OpenAI’s interface.

# Start LM Studio API server
lms server start --model codellama-7b --port 8080

You can then point any tool expecting an OpenAI-compatible API to your local endpoint:

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="codellama-7b",
    messages=[{"role": "user", "content": "Explain async/await in JavaScript"}]
)

This approach works with many tools designed for cloud APIs, allowing flexible integration.

llama.cpp: Maximum Performance on CPU and GPU

For teams that need the most out of their hardware—especially on Linux servers without a desktop environment—llama.cpp is the right choice. It runs directly as a server process with an OpenAI-compatible API:

# Download and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make -j$(nproc)

# Start the server with a quantized DeepSeek-Coder model
./server \
  --model models/deepseek-coder-33b.Q4_K_M.gguf \
  --ctx-size 8192 \
  --threads 16 \
  --port 8080 \
  --n-gpu-layers 35

The --n-gpu-layers flag offloads layers to the GPU. Set it to 0 for CPU-only inference, or match it to your VRAM capacity. On a machine with 24GB VRAM, you can fully offload a 33B Q4-quantized model.

Practical Setup for Air-Gapped Environments

Hardware Considerations

For acceptable performance, aim for:

• Minimum: 16GB RAM, modern CPU (Apple Silicon or recent Intel/AMD)

• Recommended: 24GB+ RAM, dedicated GPU (NVIDIA with 8GB+ VRAM)

• Optimal: 32GB+ RAM, NVIDIA GPU with 12GB+ VRAM

CPU-only inference works but runs slower. A 7B parameter model typically generates 10-30 tokens per second on good CPU hardware, while GPUs push 50-150+ tokens per second.

Model Selection by Use Case

Different models excel at different tasks:

|——-|———–|——|—————–|

The 7B models provide reasonable quality with modest hardware. If you have GPU resources, 13B-34B models offer meaningfully better results.

Integration Patterns

Terminal-Based Workflow

For terminal-centric developers, combine Ollama with AI command-line tools:

# Using aicommits for git commit messages
aicommits configure --provider ollama --model codellama

# Using ai-shell for command explanation
ai-shell explain "find . -name '*.py' -exec grep -l 'TODO' {} \;"

IDE Integration Example

Setting up VSCode with local AI requires installing the Continue extension and configuring it:

{
  "continue": {
    "models": [{
      "provider": "ollama",
      "model": "codellama:13b"
    }],
    "contextProviders": ["code", "file", "folder", "git", "terminal"]
  }
}

This provides:

• Inline code completion

• Chat with selected code

• Context-aware explanations

• All running offline

Limitations and Workarounds

Local models have genuine constraints compared to GPT-4 or Claude. They struggle with:

• Complex architectural advice spanning multiple files

• frameworks without training data

• Multi-step reasoning through large codebases

Mitigate these by:

1. Providing more context in prompts (include relevant code snippets)

2. Using larger models when hardware allows

3. Accepting that some tasks still benefit from cloud tools when security permits

Security and Compliance

Air-gapped local LLMs address:

• Data exfiltration concerns

• Regulatory compliance (HIPAA, FedRAMP, PCI-DSS)

• Intellectual property protection

• Audit trail requirements

Document your setup for compliance reviews. Ensure model weights come from trusted sources and verify checksums.

# Verify model integrity before deployment
sha256sum deepseek-coder-33b.Q4_K_M.gguf
# Compare with checksum published on model's official release page

Keep a record of which model version is deployed, its origin (Hugging Face model card URL, download date), and who approved it. Some FedRAMP environments also require that you run models through a software composition analysis (SCA) tool to check for supply chain risks.

Performance Benchmarks: Local vs Cloud Coding Assistance

Understanding the real performance gap helps teams make an informed decision. These benchmarks compare local models to cloud alternatives on HumanEval (Python coding benchmark) and a custom Go/Rust test suite:

The cloud models score higher on benchmarks, but the gap is smaller than many expect. For everyday tasks—completing functions, generating tests, explaining code—DeepSeek-Coder-33B handles roughly 80% of requests with quality comparable to GPT-4o. The remaining 20% (complex architecture discussions, long multi-file reasoning) still benefits from cloud models when security permits a hybrid approach.

FAQ

Q: How do I transfer model files into an air-gapped network? Download model weights to a trusted internet-connected machine, verify checksums, copy to an approved external drive or internal artifact repository, then transfer following your organization’s media sanitization procedures. Ollama models are stored as GGUF files, typically ranging from 4GB (7B Q4) to 20GB (33B Q4).

Q: Can I fine-tune a local model on my organization’s codebase? Yes. Tools like Axolotl and LLaMA-Factory support LoRA fine-tuning on consumer hardware. A 7B model can be fine-tuned on a single 24GB GPU in 2-4 hours. This is especially valuable if your codebase uses proprietary frameworks or domain-specific patterns the base model hasn’t seen.

Q: Which local model works best for non-Python languages like Go, Rust, or Java? DeepSeek-Coder and Qwen2.5-Coder both trained on diverse language datasets. For Go specifically, DeepSeek-Coder-33B scores well on idiomatic pattern generation. Qwen2.5-Coder-14B is a good balance for teams with mixed language stacks and moderate hardware.

Q: Is there a way to get IDE autocomplete working without an API server? Continue.dev supports direct Ollama integration without running a separate server process. Install Ollama, pull your model, and Continue.dev communicates with Ollama’s native socket. This reduces latency and eliminates port management for single-developer setups.

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