AI Tools Compared

If you use Neovim as your primary editor, you have likely explored ways to integrate AI-powered code completion. Many developers turn to cloud-based solutions like GitHub Copilot, but these require API keys, subscriptions, and an internet connection. For privacy-conscious developers, teams working in secure environments, or anyone wanting to avoid recurring costs, self-hosted alternatives offer a compelling path forward.

This article compares the leading open-source AI code completion tools for Neovim that run locally without cloud API dependencies. Each solution has distinct trade-offs in setup complexity, model quality, and resource requirements.

Why Skip Cloud API Keys?

Cloud-based AI code completion services send your code to external servers. This raises legitimate concerns around data privacy, especially when working with proprietary codebases. Additionally, reliance on external services means your completion workflow depends on internet connectivity and service availability.

Running AI code completion locally gives you complete control over your data. You can use the tools on airplanes, in secure facilities, or in regions with limited internet access. The upfront investment in hardware and setup pays off over time with no per-token costs.

CodeLLama (via Ollama)

Ollama has become the simplest way to run large language models locally. It supports CodeLLama, a model specifically designed for code generation and completion.

Setup

First, install Ollama on your system:

# macOS
brew install ollama

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

Pull the CodeLLama model:

ollama pull codellama

Neovim Integration

Use the completor plugin or configure nvim-cmp with a custom source. Here is a basic setup using nvim-cmp:

-- ~/.config/nvim/lua/plugins/cmp.lua
return {
  "hrsh7th/nvim-cmp",
  dependencies = { "hrsh7th/cmp-ollama" },
  config = function()
    local cmp = require("cmp")
    cmp.setup({
      sources = cmp.config.sources({
        { name = "ollama" },
      }),
    })
  end,
}

You will need to configure the Ollama endpoint and model in your setup. This approach provides completions from a model running entirely on your machine.

CodeGeex

CodeGeex is an open-source code generation model maintained by the THUDM research group. It specializes in code completion and supports multiple programming languages.

Installation

CodeGeex can be deployed as a local server using the official implementation:

git clone https://github.com/THUDM/CodeGeeX
cd CodeGeeX
pip install -r requirements.txt

Run the server locally:

python codegeex/benchmark/api.py --port 8080

Neovim Integration

Connect Neovim using a custom completion source. You can use vim-go or write a simple Python script that interfaces with the API:

-- Simple example using an HTTP request
local function get_completion(prompt)
  local http = require("socket.http")
  local json = require("cjson")

  local response = {}
  local body = json.encode({
    prompt = prompt,
    max_length = 256,
  })

  local result = http.request({
    url = "http://localhost:8080/completions",
    method = "POST",
    headers = {
      ["Content-Type"] = "application/json",
      ["Content-Length"] = #body,
    },
    source = ltn12.source.string(body),
    sink = ltn12.sink.table(response),
  })

  return table.concat(response)
end

This requires additional Lua dependencies, but demonstrates the core approach.

CodeGen (Salesforce)

CodeGen was developed by Salesforce Research and is available in multiple sizes. The model excels at code generation tasks and can run locally through various frameworks.

Running with Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Salesforce/codegen-2b-mono"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def complete_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=50)
    return tokenizer.decode(outputs[0])

For Neovim integration, wrap this Python script in a VimEx command or use the completor framework to create a custom source.

StarCoder

StarCoder is a code-focused language model developed by BigCode. It was trained on permissively licensed code from GitHub, making it a responsible choice for commercial projects.

Quick Start with llama.cpp

For running StarCoder efficiently on consumer hardware, use the quantized version with llama.cpp:

# Download the quantized StarCoder model
wget https://huggingface.co/TheBloke/StarCoder-GGML/resolve/main/starcoder-q4_0.gguf

# Run with llama.cpp
./main -m starcoder-q4_0.gguf --n-gpu-layers 1 -c 2048

Configure your Neovim completion plugin to query this local endpoint. The setup requires more configuration than Ollama but provides more control over the model behavior.

Qwen2.5-Coder

Qwen2.5-Coder (from Alibaba’s Qwen team) emerged as a strong contender in late 2025. The 7B and 14B variants perform competitively with CodeLLama 34B on most benchmarks while running comfortably on consumer hardware.

Setup via Ollama

ollama pull qwen2.5-coder:7b

Then configure your completion source to use qwen2.5-coder as the model name. The model has notably better instruction-following for fill-in-the-middle (FIM) tasks than earlier Qwen iterations, which translates to more accurate inline completions rather than pure next-token generation.

Neovim Plugins Purpose-Built for Local Completion

Beyond generic cmp sources, several plugins are specifically designed for local LLM completion workflows:

For daily driver use, llm.nvim with streaming enabled provides the most responsive feel. Latency on a 7B quantized model running on Apple Silicon or a mid-range NVIDIA GPU is typically under 500ms for short completions—fast enough to not disrupt flow.

Comparing the Options

Tool Model Size Hardware Requirements Setup Complexity
Ollama + CodeLLama 7-13B parameters 8-16GB RAM recommended Low
Ollama + Qwen2.5-Coder 7-14B parameters 8-16GB RAM Low
CodeGeex 13B parameters 16GB+ RAM Medium
CodeGen 2-16B parameters 4-16GB RAM Medium
StarCoder + llama.cpp Quantized options 4-8GB RAM Medium-High

Ollama provides the easiest setup experience. If you want minimal configuration time, CodeLLama or Qwen2.5-Coder through Ollama delivers the fastest path to working local AI code completion.

CodeGeex and CodeGen offer specialized code models but require more manual setup. These work well if you need specific capabilities from these architectures.

StarCoder excels when you need the most efficient resource usage. The quantized versions run smoothly on laptops without dedicated GPUs.

Hardware Considerations

Local AI code completion demands computational resources. A modern multi-core CPU can handle smaller models, but GPU acceleration dramatically improves response times. NVIDIA GPUs with CUDA support work best. Apple Silicon Macs can also run these models efficiently through Metal acceleration in some frameworks.

RAM usage scales with model size. Plan for at least 8GB of available memory for smaller models, and 16GB or more for larger implementations.

Performance Tips

To get the best experience from local AI code completion in Neovim:

Advanced Configuration: Building a Production Setup

For developers committed to self-hosted completions, here’s a production-ready architecture:

Infrastructure Stack:

Neovim Editor
  ↓
nvim-cmp (completion engine)
  ↓
Custom Lua plugin (talks to inference server)
  ↓
vLLM or Ollama (inference server)
  ↓
Quantized CodeLLama model (GPU inference)

Example: Ollama + nvim-cmp Setup

First, install Ollama and pull a model:

# Install Ollama (https://ollama.ai)
ollama pull codellama:7b-instruct

# Start Ollama service
ollama serve --host 127.0.0.1:11434

Then configure Neovim (~/.config/nvim/init.lua):

-- Install packer if needed, then add these plugins
use 'hrsh7th/nvim-cmp'
use 'hrsh7th/cmp-nvim-lsp'
use 'hrsh7th/cmp-buffer'
use 'hrsh7th/cmp-path'

-- Custom completion source for Ollama
local cmp = require('cmp')
local source = {}

function source.new()
  return setmetatable({}, { __index = source })
end

function source:complete(params, callback)
  local prefix = params.context.line:sub(params.offset)

  -- Call Ollama API
  local cmd = string.format(
    "curl -s http://127.0.0.1:11434/api/generate -d '{\"model\": \"codellama:7b-instruct\", \"prompt\": \"%s\", \"stream\": false}' | jq -r '.response'",
    prefix
  )

  local handle = io.popen(cmd)
  local result = handle:read("*a")
  handle:close()

  -- Parse completion and return
  local items = {{
    label = result,
    kind = cmp.lsp.CompletionItemKind.Text
  }}

  callback({ items = items })
end

function source:resolve(completion_item, callback)
  callback(completion_item)
end

function source:execute(completion_item, callback)
  callback(completion_item)
end

-- Register the source
cmp.register_source('ollama', source.new())

-- Configure cmp
cmp.setup({
  sources = cmp.config.sources({
    { name = 'ollama', priority = 10 },
    { name = 'nvim_lsp' },
    { name = 'buffer' },
  })
})

Production Considerations:

  1. Response Caching: Cache completions for identical context to reduce latency ```lua local completion_cache = {}

function get_completion_cached(prompt) if completion_cache[prompt] then return completion_cache[prompt] end local result = get_completion(prompt) completion_cache[prompt] = result return result end


2. **Timeout Handling**: Set timeouts for inference to prevent hanging
```bash
# In Ollama startup
timeout 5 curl http://127.0.0.1:11434/api/generate ...
  1. Error Recovery: Gracefully fall back to LSP if AI completion fails ```lua function source:complete(params, callback) local success, result = pcall(function() return call_ollama(params) end)

if not success then – Fall back to LSP completion vim.lsp.completion.trigger(params) else callback(result) end end


## Model Selection for Different Languages

Not all models excel equally across languages:

**For Python/Go/Rust:**
- CodeLLama 13B (best overall balance)
- DeepSeek Coder (if you have GPU resources)
- StarCoder 15B (good alternative)

**For JavaScript/TypeScript:**
- Qwen Coder (excellent for JS/TS)
- CodeLLama 13B+ (needs more VRAM)
- CodeGeex (specialized for JS)

**For C/C++/Java:**
- CodeLLama 70B (if you have resources)
- StarCoder (surprisingly good for C++)
- Specialized Java model if available

## Benchmarking Your Setup

Before deploying, benchmark completion quality and speed:

```bash
#!/bin/bash
# Benchmark completion latency

MODEL="codellama:7b"
ITERATIONS=10

echo "Benchmarking $MODEL..."

for i in $(seq 1 $ITERATIONS); do
  prompt="def factorial(n):"

  time_start=$(date +%s%N)

  curl -s http://127.0.0.1:11434/api/generate \
    -d "{\"model\": \"$MODEL\", \"prompt\": \"$prompt\", \"stream\": false}" > /dev/null

  time_end=$(date +%s%N)
  time_ms=$(( (time_end - time_start) / 1000000 ))

  echo "Iteration $i: ${time_ms}ms"
done

Track:

Troubleshooting Common Issues

Issue: Completions are very slow (>5s)

Issue: Out of memory errors

Issue: Completions don’t match the current context

Integration with Other Tools

GitHub Copilot Comparison: | Aspect | Local Completion | GitHub Copilot | |——–|——————|—————–| | Privacy | Complete (local) | Data sent to GitHub | | Cost | ~$100-200/year electricity | $10/month | | Quality | Good (7-13B models) | Excellent (larger models) | | Setup | 2-4 hours | 5 minutes | | Speed | 1-5 seconds/completion | <500ms | | Offline | Yes | No |

Local completion makes sense when privacy matters more than raw speed, or when you want to avoid recurring subscription costs.

Built by theluckystrike — More at zovo.one

Frequently Asked Questions

Who is this article written for?

This article is written for developers, technical professionals, and power users who want practical guidance. Whether you are evaluating options or implementing a solution, the information here focuses on real-world applicability rather than theoretical overviews.

How current is the information in this article?

We update articles regularly to reflect the latest changes. However, tools and platforms evolve quickly. Always verify specific feature availability and pricing directly on the official website before making purchasing decisions.

Does Neovim offer a free tier?

Most major tools offer some form of free tier or trial period. Check Neovim’s current pricing page for the latest free tier details, as these change frequently. Free tiers typically have usage limits that work for evaluation but may not be sufficient for daily professional use.

How do I get started quickly?

Pick one tool from the options discussed and sign up for a free trial. Spend 30 minutes on a real task from your daily work rather than running through tutorials. Real usage reveals fit faster than feature comparisons.

What is the learning curve like?

Most tools discussed here can be used productively within a few hours. Mastering advanced features takes 1-2 weeks of regular use. Focus on the 20% of features that cover 80% of your needs first, then explore advanced capabilities as specific needs arise.

Explore Our Developer Guides

© 2026 theluckystrike Developer Guides