Best Local Coding LLMs for 8GB, 16GB, and 24GB VRAM

Top pick: Qwen2.5-Coder 7B Instruct is the most practical starting point for local coding on GPUs with 8GB, 16GB, or 24GB of VRAM: its quantized versions fit modest cards, and it handles common programming and editing tasks well. If you have 24GB and want stronger results on complex work, step up to Qwen2.5-Coder 14B or a quantized 32B model, accepting slower responses and tighter memory limits.

What to Look For

VRAM means more than model size. A model’s parameter count is not its complete memory footprint. Quantization reduces the space used by its weights, but the runtime also needs memory for the context (the prompt and conversation), temporary calculations, and sometimes GPU display or other applications. A model that barely loads may run out of memory when you give it a long file or ask it to inspect a large codebase.

Quantization and quality: GGUF files in formats such as Q4_K_M are popular with llama.cpp-based apps, while EXL2 and GPTQ are commonly used with other inference tools. Four-bit quantization is often a sensible balance on consumer GPUs; lower-bit versions can fit more easily but may reduce answer quality. Names and available quantizations vary by release, so check the exact model card and file before downloading.

Context length: Advertised context limits are not a promise that the model will use that many tokens on your GPU. Longer context consumes additional memory and can slow generation. For everyday coding, a responsive model with enough room for the relevant function or files is often more useful than a nominally huge context window.

Task fit and tooling: Chat/instruct models are convenient for questions, code review, and edits. Fill-in-the-middle (FIM) support helps code-completion tools insert code inside an existing file. Also consider your inference app, operating system, GPU backend, and whether you need a model that can work offline. Do not assume every quantization supports every feature or loader.

Qwen2.5-Coder 7B Instruct

Best all-around choice for 8GB. This code-focused instruct model is a useful balance of capability, size, and speed. A four-bit quantization is a sensible first try on an 8GB card, especially with a moderate context and no large GPU-heavy applications running. It is also an easy, relatively quick option on 16GB and 24GB cards when you value responsiveness over maximum model capacity.

Trade-offs: It will not match a larger model on difficult debugging, architectural decisions, or tasks requiring broad project understanding. The exact fit depends on the quantization, context length, runtime, and GPU memory reserved by the system. Choose it for autocomplete, small changes, explanations, and everyday coding rather than expecting it to replace a larger assistant on demanding work.

Qwen2.5-Coder 14B Instruct

Best step up for 16GB and a strong fit for 24GB. The 14B version offers more capacity than 7B while remaining much more manageable than 32B. On 16GB, use a quantized build and keep context moderate; on 24GB, you have more room to try a higher-quality quantization or longer prompts, though neither is guaranteed to fit alongside every runtime and workload.

Trade-offs: Generation is generally slower and memory use higher than with 7B. It is a good choice for multi-file edits, debugging, and code explanations when you can tolerate the extra wait. If an 8GB card is your only GPU, this is usually not the comfortable first choice; consider a smaller quantization only after checking real memory requirements and accepting reduced headroom.

Qwen2.5-Coder 32B Instruct

Best quality-first option among these picks for 24GB. A 32B model can give more capable responses on complicated coding questions than smaller local models, making it worth considering for substantial refactoring, reasoning through bugs, or reviewing larger snippets. On a 24GB GPU, look for a suitable low-bit quantization and expect to manage context and other GPU workloads carefully.

Trade-offs: Do not assume every 32B four-bit file will fit comfortably in 24GB. Weight format, quantization, context, software overhead, and available memory all matter. It may run slowly, require partial CPU offload, or fail to load at the context setting you want. CPU offload can make a model usable, but it often reduces speed. This is not a practical recommendation for 8GB, and it is a stretch for many 16GB setups.

DeepSeek-Coder 6.7B Instruct

Best smaller alternative when compatibility or a lighter model matters. DeepSeek-Coder 6.7B Instruct is a recognizable code model that can suit 8GB cards in a suitable quantization and works on larger cards with more room for context. It is worth comparing if your preferred app already supports a compatible release or if you find a quantization that fits your setup.

Trade-offs: It is an older model family than Qwen2.5-Coder and may be less capable on some current coding tasks. Check the particular model and license details rather than treating every community conversion as identical. Pick it for a compact coding assistant, not simply because the name suggests it is universally better or faster.

StarCoder2 7B

Best to consider for code completion and FIM-oriented workflows. StarCoder2 7B is designed for code and can be a good match for editor integrations that use fill-in-the-middle completion. Its smaller size makes quantized versions more approachable on 8GB, with additional room on 16GB or 24GB for context and the rest of the runtime.

Trade-offs: Its fit depends on the application’s support for the model’s completion format; a chat interface may not show its strengths as clearly as an editor completion workflow. It is not automatically the strongest option for conversational debugging or complex agent-style tasks. Choose it when your tool specifically benefits from FIM, and compare it with Qwen2.5-Coder 7B for general chat and edits.

How to Choose

With 8GB VRAM: Start with a 7B-class model, such as Qwen2.5-Coder 7B Instruct, in a four-bit quantization. Keep context moderate, close GPU-heavy apps, and leave memory headroom rather than chasing the largest file that technically loads. If the model runs out of memory, lower context or choose a smaller quantization before changing everything else.

With 16GB VRAM: Compare 7B for speed with 14B for stronger answers. A 14B quantized model is often the useful upgrade, but the best configuration depends on context and runtime. If you work mainly in autocomplete or make quick edits, the smaller model may feel better day to day.

With 24GB VRAM: Try 14B first if you want a reliable balance. Consider 32B when answer quality matters more than speed and you are willing to test quantization, context, and possible CPU offload. More parameters do not guarantee better results for every language, repository, or prompt.

Match the model to the job. Use a smaller model for completion, boilerplate, and short edits; choose a larger one for difficult debugging or reasoning over multiple files. For whole-project work, provide relevant files and clear instructions: a large context window does not ensure the model has understood the codebase. Test with your actual editor and tasks before settling on a setup.

Budget beyond the GPU. Local inference is not only a VRAM question: system RAM, storage for model files, cooling, and the speed of your GPU also affect the experience. Many local inference apps support downloadable model files, but check that the app can load the file format and quantization you choose. Model releases and their licenses can change; read the current model card, especially before using a model in commercial work.

Frequently Asked Questions

Can an 8GB GPU run a coding LLM?
Yes. Start with a quantized 7B model and moderate context. Actual fit depends on the model file, runtime, and memory already in use.

Is 4-bit quantization good enough for coding?
It is a practical starting point for local use and often preserves useful capability. If answers are weak, compare another model or quantization on your own tasks rather than assuming more bits always solve the problem.

Will a 24GB card run a 32B model?
Sometimes, with a suitable quantization and controlled context. Memory overhead varies, so verify the specific file and expect that some configurations may need CPU offload or lower settings.

Which model is best for VS Code autocomplete?
Try a model and extension that support FIM; StarCoder2 7B is one candidate. Compatibility depends on the editor extension and how it serves the model.

Do local coding models send my code online?
A model running fully on your machine can generate locally, but the app, extensions, or other services may still make network requests. Review their settings and documentation, and test offline if local-only operation is essential.

Bottom Line

For most people, begin with Qwen2.5-Coder 7B Instruct: it is the safest practical recommendation across the three VRAM tiers when quantized appropriately. On 16GB, move to 14B if you want more capability; on 24GB, test 14B for balance or 32B for quality-first work. On 8GB, prioritize a model that fits with room for context over a larger model that barely loads.

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