Best Local AI Models for SQL Generation
Translating natural language to correct, performant SQL across dialects.
For SQL generation, Qwen 2.5 Coder 14B is the best choice for its unparalleled accuracy and performance, but if you have limited VRAM, consider Code Llama 7B for a balanced and efficient solution.
SQL generation requires an AI model that can accurately translate natural language into performant SQL queries across various database dialects. Users should prioritize models with high accuracy, low latency, and efficient memory usage. Running these models locally offers significant advantages over APIs, including data privacy, reduced latency, and the ability to customize and fine-tune the model to specific use cases.
Top picks
- #1
Qwen 2.5 Coder 14B14B · apache-2.0 · min 8.9GB
The most powerful and accurate model for complex SQL generation.
Qwen 2.5 Coder 14B stands out as the top pick for SQL generation due to its massive 14 billion parameters and high-quality output. With a minimum VRAM requirement of 8.9GB, it can handle even the most complex and nuanced SQL queries with ease. Its Apache-2.0 license makes it highly accessible for both commercial and open-source projects. While it demands more hardware resources, the trade-off is well worth it for applications requiring high precision and performance. The only caveat is that users with limited VRAM might need to consider a smaller model or upgrade their hardware.
- #2
Code Llama 7B7B · llama2 · min 4.3GB
A strong contender with a balance of size and performance.
Code Llama 7B is a robust alternative to the top pick, offering a balanced combination of performance and resource efficiency. With 7 billion parameters and a minimum VRAM requirement of 4.3GB, it strikes a sweet spot between accuracy and hardware demands. Licensed under the LLaMA 2 license, it is suitable for a wide range of use cases. While it may not match the 14B model in handling extremely complex queries, it provides excellent results for most SQL generation tasks, making it a versatile choice for users with moderate hardware capabilities.
- #3
DeepSeek Coder 6.7B6.7B · mit · min 4.3GB
High performance with a permissive MIT license.
DeepSeek Coder 6.7B is another strong option, boasting 6.7 billion parameters and a minimum VRAM requirement of 4.3GB. Its MIT license offers flexibility and ease of integration into various projects. This model excels in generating accurate and efficient SQL queries, making it a solid choice for users who value both performance and licensing freedom. It is slightly less powerful than the 14B Qwen model but remains a top-tier option for most SQL generation needs.
- #4
StarCoder2 7B7B · bigcode-openrail-m · min 4.7GB
Efficient and reliable with a focus on code generation.
StarCoder2 7B is a reliable model with 7 billion parameters and a minimum VRAM requirement of 4.7GB. Licensed under the BigCode OpenRail-M license, it is designed with a strong focus on code generation, including SQL. This model delivers consistent and accurate SQL queries, making it a good choice for users who need a balance of performance and resource efficiency. While it may not be as powerful as the top picks, it is a dependable option for a wide range of SQL generation tasks.
- #5
Qwen 2.5 Coder 7B7.6B · apache-2.0 · min 4.9GB
A lightweight yet powerful model for resource-constrained environments.
Qwen 2.5 Coder 7B is a lightweight yet powerful model with 7.6 billion parameters and a minimum VRAM requirement of 4.9GB. Its Apache-2.0 license ensures easy integration into various projects. This model is particularly useful for users with limited hardware resources who still require high-quality SQL generation. While it may not match the performance of larger models, it offers a compelling balance of accuracy and resource efficiency, making it a solid choice for many use cases.
Hardware guidance
For SQL generation, users should aim for at least 8GB of VRAM to run most models efficiently. For optimal performance, 12GB of VRAM is recommended, especially for models like Qwen 2.5 Coder 14B. Users with more demanding tasks or larger datasets should consider 16GB or 24GB+ of VRAM to ensure smooth operation and faster query generation.
When to skip local
While local models offer significant advantages, they may still lose to hosted APIs in scenarios where real-time performance and scalability are critical. Hosted services like Anthropic's Claude or Anthropic's AI21 Labs can provide seamless integration and automatic scaling, which might be necessary for large-scale or mission-critical applications.
Need a guide for a different use case? See all 50 buyer's guides →