Images by editor | chatgptWe live in an age where large-scale language models (LLMs) dominate and influence the way we work. Even local LLMs that were fine-tuned for coding have become increasingly effective, allowing developers and data experts to use them as personal coding assistants in their own environments. This approach is often desirable as these models can enhance data privacy and reduce API costs.
These local coding LLMs offer practical AI assistance directly to developer workflows, and thus have a variety of applications that were previously unpractical. This allows inline autocomplete, code debugging, and even project-wide inference. If you're interested, there are many ways to run LLM locally, so check it out.
Even people with non-developers and no technical backgrounds have emerged for local coding LLMS, a new trend called Vibe coding has emerged in the local scene. If you are a data scientist, take a look at some of the projects you can build with vibe coding.
As local coding LLM becomes more prominent, it's helpful to know what options you can do on your own. In this article, we will investigate some of the best local coding LLMs that fit local workflows and highlight why they stand out from others.
# 1. GLM-4-32B-0414
Tsinghua University's Zhipu AI recently introduced a new series of open source models GLM-4-32B-0414a 3.2 billion parameter model comparable to GPT-4O and DeepSeek-V3. This model is widely assumed with 15t of inference-heavy data and is refined through human preference adjustment, rejection sampling, and reinforcement learning. This helps the model follow the instructions and produce a well-structured output.
This model is excellent at handling complex code generation, code analysis, and function-style output. Thanks to training, you can perform multi-step inference in your code, such as suggesting better trace logic and improvements over many models of similar or larger sizes. Another advantage is that it has a relatively large context window of up to 32K tokens, allowing GLM-4 to process large amounts of code or multiple files without any issues. This can be useful for tasks such as analyzing the entire codebase or providing comprehensive refactoring suggestions in one run.
# 2. deepseekcoderv2
DeepSeekCoder V2 Coding LLM based on an emper of exper mixed system specially trained for coding tasks. The model will be released in two open weight variants: the 16B “Light” model and the 236B model. The DeepSeekCoder V2 model was pretrained with 6T additional data on top of the DeepSeek-V2, extending language coverage to 86-338 programming languages. The context window is also extended to 128K tokens. This is useful for understanding the entire project, code filling, and cross-file refactoring.
Performance-wise, the model shows top layer results, as demonstrated by the strong Aider LLM leaderboard score, and places it together with the premium closed model for code inference. The code is a MIT license and the model weights are available under the DeepSeek model license, which allows for commercial use. 236B is running 16B light locally for fast code completion and vibe coding sessions, while 236B is targeted for multi-GPU servers for heavy code generation and project-scale inference.
# 3. QWEN3-CODER
QWEN3-CODER It is a code-centric LLM developed by the Qwen team at Alibaba Cloud, trained on 7.5T data, 70% of which was code. I use a Mixture (MOE) transformer with two versions of the 35B and 480B parameters. Its performance rivals the coding capabilities of the GPT-4 level and Claude 4 sonnet, resulting in a 256K context window (expandable to 1M via Yarm). This allows the model to process the entire repository and long files in one session. It also boasts the capabilities of agent coding tasks, while understanding and generating code in over 350 programming languages.
The 480B model requires heavy hardware such as a Multi-H100 GPU or high memory server, but in MOE designs, only a subset of the parameters are active per token. If the requirements are small, the 35B and FP8 variants can run on a single high-end GPU for local use. Model weights are openly available under the Apache 2.0 license, making QWEN3-Coder a powerful yet accessible coding assistant.
# 4. Codestral
Code Strull This is a dedicated code transformer tailored for code generation in over 80 programming languages developed by Mistral AI. It was introduced in two variations: 22B and MAMBA 7B. These are designed for lower latency compared to their size. This is useful during live editing. Weights is available for download with Mistral's unproduction license (free for research/testing) and requires a separate license for commercial use.
For local coding, 22B is capable, fast enough for 4-8 bits on a single powerful GPU for daily use, and a long generation continues to exist for larger projects. Mistral also provides Codestral's endpoints, but if you stay completely local, open weights and a general inference stack are already sufficient.
# 5. Cordrama
Cordrama A family of fine-tuned models for llama-based coding, with multiple sizes (7b, 13b, 34b, 70b) and variations developed by Meta (base, Python specialization, directive). Depending on the version, the model can reliably work for certain uses, such as filling and Python-specific tasks, even for very long inputs (up to 100k for long context techniques). All are available as open weights under Meta's community license. This allows for a wide range of research and commercial use.
Code Llama is a popular baseline for local coding agents and IDE Copilots, as the 7B/13B size runs comfortably on single GPU laptops and desktops (especially when quantized). In comparison, the 34B/70B size offers more powerful accuracy when there is more VRAM. There are many applications in different versions. For example, Python models are good for data and machine learning workflows, while instructional Variant works well with editorial conversation and atmosphere flow.
# I'll summarize
As a reference to what was discussed above, this is an overall comparison of the models covered.
Click to enlarge
Depending on your requirements and local performance, these models can effectively support your work.
I hope this helped!
Cornelius Judas Ujaya Data Science Assistant Manager and Data Writer. While working full-time at Allianz Indonesia, he loves to share data tips with Python via social and writing media. Cornellius writes about a variety of AI and machine learning topics.
