Best Local AI Models for Coding & Software Development
Writing, completing, refactoring, and debugging code across multiple languages.
For the best experience in coding and software development, use Codestral 22B Abliterated v3 if you have the necessary hardware. If not, Qwen 2.5 Coder 14B is a strong alternative that balances power and efficiency.
Coding and software development require AI models that can handle complex programming tasks, such as writing, completing, refactoring, and debugging code across multiple languages. Users should optimize for models that offer high accuracy, low latency, and efficient resource usage. Running these models locally ensures data privacy, reduces dependency on internet connectivity, and provides faster response times, which are crucial for a smooth development workflow.
Top picks
- #1
Codestral 22B (abliterated)22B · other · min 12.9GB
The ultimate powerhouse for coding, combining massive size with unparalleled performance.
Codestral 22B Abliterated v3 stands out as the top pick for coding and software development due to its massive 22 billion parameters and exceptional quality rating of 100%. With a minimum VRAM requirement of 12.9GB, it demands robust hardware but delivers unmatched capabilities in handling complex programming tasks. Its open-source license (other) allows for flexible use, making it ideal for developers who need the most powerful tool available. While it may not be suitable for systems with limited resources, its comprehensive understanding of multiple programming languages and advanced code generation make it the best choice for those who can support its requirements.
- #2
Qwen 2.5 Coder 14B14B · apache-2.0 · min 8.9GB
A strong contender with a balanced mix of power and efficiency.
Qwen 2.5 Coder 14B is a formidable runner-up, boasting 14 billion parameters and a quality rating of 98%. It requires a minimum of 8.9GB VRAM, making it more accessible than the top pick while still delivering high performance. Licensed under Apache-2.0, it offers a reliable and versatile solution for coding tasks. This model excels in generating accurate and contextually relevant code, making it an excellent choice for developers who need a powerful yet manageable tool for their projects.
- #3
Code Llama 13B Instruct13B · llama2 · min 7.8GB
A solid choice with a slight edge in efficiency.
Code Llama 13B Instruct is a strong third-place contender, with 13 billion parameters and a quality rating of 85%. It requires 7.8GB VRAM, making it slightly more efficient in terms of resource usage compared to the top two picks. Licensed under LLaMA2, it offers a good balance between performance and resource consumption. While its quality rating is slightly lower, it remains a highly capable model for coding tasks, particularly for users who prioritize efficiency without compromising too much on performance.
- #4
DeepSeek Coder 6.7B6.7B · mit · min 4.3GB
A lightweight yet powerful option for mid-range systems.
DeepSeek Coder 6.7B is a lightweight yet powerful model with 6.7 billion parameters and a quality rating of 98%. It requires only 4.3GB VRAM, making it suitable for mid-range systems. Licensed under the MIT license, it offers flexibility and ease of use. This model is particularly strong in generating concise and accurate code, making it an excellent choice for developers working on systems with moderate resources. Its efficiency and reliability make it a solid pick for a wide range of coding tasks.
- #5
StarCoder2 7B7B · bigcode-openrail-m · min 4.7GB
A compact and efficient model for resource-constrained environments.
StarCoder2 7B is a compact and efficient model with 7 billion parameters and a quality rating of 98%. It requires 4.7GB VRAM, making it suitable for systems with limited resources. Licensed under the BigCode OpenRail-M license, it offers a balance between performance and resource usage. This model excels in generating accurate and contextually relevant code, making it a great choice for developers working on smaller or less powerful systems. Its efficiency and reliability make it a solid pick for a wide range of coding tasks, especially when resource constraints are a concern.
Hardware guidance
For coding and software development, the hardware you choose should align with the complexity of your tasks and the resources required by the AI model. For basic tasks, a system with 8GB VRAM is sufficient, making models like StarCoder2 7B a good fit. For more demanding tasks, a system with 12GB VRAM can handle models like DeepSeek Coder 6.7B or Code Llama 7B. For advanced tasks, a system with 16GB VRAM is recommended for models like Qwen 2.5 Coder 14B, and for the most resource-intensive tasks, a system with 24GB+ VRAM is ideal for running Codestral 22B Abliterated v3.
When to skip local
While local models offer significant advantages, there are scenarios where hosted APIs might be preferable. For instance, if you have limited computational resources or need to scale quickly, hosted APIs like GitHub Copilot or Anthropic's Claude can provide powerful coding assistance without the need for high-end hardware. These services also benefit from regular updates and maintenance, ensuring you always have access to the latest features and improvements.
Need a guide for a different use case? See all 50 buyer's guides →