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The best local coding model depends on what your GPU can fit after accounting for more than model weights. With 8GB VRAM, start with a compact coder such as Qwen2.5-Coder 7B or StarCoder2. At 16GB, test larger quantized candidates such as DeepSeek-Coder-V2-Lite 16B or Devstral 2 22B. A 24GB card makes larger options, including Qwen3-Coder-30B-A3B-Instruct, more practical—but none is a guaranteed fit at every context length or runtime setting. Treat these as candidates to test on your own machine, not a universal performance ranking.
Which local coding model fits each VRAM tier?
Published sizing figures are estimates for particular quantized artifacts, not guarantees for every download, runtime, GPU, or context setting. The figures below describe weight or artifact size; they do not include the full memory cost of context cache, runtime, or other applications using the GPU.
| Available VRAM | Candidate models | Published size estimate | Practical interpretation |
|---|---|---|---|
| 8GB | Qwen2.5-Coder 7B; StarCoder2 7B | Local AI Models estimates roughly 4.6GB for Qwen2.5-Coder 7B Q4 weights and 4.2GB for StarCoder2 7B Q4 weights in its 2026 sizing guide: Local AI Models. | Start with a compact coder for autocomplete, simple code questions, and smaller tasks. The remaining VRAM still has to cover context cache, runtime, and any concurrent GPU use. |
| 16GB | DeepSeek-Coder-V2-Lite 16B; Devstral 2 22B | Local AI Models estimates about 9.6GB for DeepSeek-Coder-V2-Lite 16B Q4 weights in its 2026 guide; LLM Configurator lists about 14.1GB for a Devstral 2 22B Q4 artifact in its 2026 guide: Local AI Models; LLM Configurator. | These are separate sizing examples, not a head-to-head result. Check the exact artifact and test the context you intend to use before settling on either. |
| 24GB | Qwen3-Coder-30B-A3B-Instruct and other models in a similar size class | Local AI Models reports roughly 18GB for Qwen3-Coder-30B-A3B-Instruct Q4 weights in its 2026 dataset: Local AI Models. | Larger models become more feasible, but an 18GB weight estimate leaves less space for context and runtime than the card’s headline capacity suggests. |
Ollama’s coding-model catalog includes Qwen3-Coder, Qwen2.5-Coder, DeepSeek-Coder, DeepSeek-Coder-V2, and OpenCoder in multiple sizes. Catalog availability identifies options to investigate; it does not establish their coding quality or whether a particular artifact will fit your setup: Ollama model library.
Why a model’s weight size does not tell you whether it will run well
Three main demands compete for GPU memory: model weights, the key-value (KV) cache used to hold context, and other GPU work such as an editor, another model, or applications. Quantization can reduce weight storage, but Q4 estimates vary by artifact. The cache also grows with context length, so a model that loads at 8K context may not load—or may become unacceptably slow—at 32K.
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WhatLLM.org recommends keeping roughly 15–25% of memory free as practical headroom and starting with 16K or 32K context, increasing it only if repository work needs more. This is guidance, not a universal measured threshold: WhatLLM.org. Longer contexts can consume substantial extra memory, and coding-agent loops may add tool output, prompts, and source files on top of the repository itself.
Published maximum context is not a promise that the full context will operate within your available VRAM. Test the actual editor or agent, runtime, quantized artifact, and context settings you plan to use.
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What to expect from each tier
8GB VRAM: favor focused, smaller tasks
Qwen2.5-Coder 7B and StarCoder2 7B are plausible starting candidates because their cited Q4 weight estimates leave some capacity beyond the weights. That does not mean all of that remainder is available for context: runtime and other GPU use take a share too. Expect to evaluate them for autocomplete, explanations, and contained edits rather than assume they will handle long-context, multi-file agent work comfortably.
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The estimates for DeepSeek-Coder-V2-Lite 16B and Devstral 2 22B illustrate why the model name and parameter count alone are not enough. Their cited Q4 figures differ substantially, and come from separate guides rather than a common benchmark or identical test setup. Check the exact downloadable artifact and load it with a representative context before choosing.
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- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5060
- Integrated with 8GB GDDR7 128bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
24GB VRAM: larger models are possible, not effortless
Local AI Models describes Qwen3-Coder-30B-A3B-Instruct as 30.5 billion total parameters and 3.3 billion active parameters, with roughly 18GB of Q4 weights in its 2026 dataset: Local AI Models. Because it is a mixture-of-experts model, the active-parameter count does not mean only 3.3 billion parameters need to be stored; memory fit relates to the total stored weights. A 24GB GPU can therefore be a useful tier for larger models, but the cited weight estimate alone does not establish what context length or runtime settings will fit.
How to choose between models that fit
Once a candidate loads, judge it against your actual work rather than a single headline benchmark. The cited guides do not provide a consistent, same-hardware and same-harness comparison across the 8GB, 16GB, and 24GB picks. Some reported SWE-bench scores come from different publishers and harnesses, so combining them into one ranking would be misleading.
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- Confirm the artifact and headroom. Check the downloaded quantization’s actual size, then account for runtime, cache, and other GPU use.
- Match the task. Evaluate autocomplete, single-file assistance, and agentic multi-file edits separately; success on one does not prove suitability for another.
- Set a usable context. Measure fit and responsiveness at the context length your repository tasks require, not only at the model’s advertised maximum.
- Check latency and throughput. A model that loads but responds too slowly for your workflow may not be a practical choice.
- Run a small private evaluation. Use real issues from your repository. Include tasks where the model must revise a failed attempt, handle a misleading result, or recover from an incorrect edit—not just generate new code.
- Verify the license for your intended use. The cited guide lists Qwen3-Coder and Devstral Small 2 as Apache 2.0, while DeepSeek-Coder-V2 weights use DeepSeek’s model license; StarCoder2 and Codestral also have their own terms. Verify the exact release and license before commercial deployment.
When local hardware is not enough
If your target model does not fit with useful context and headroom, choose a smaller candidate or move the workload to hardware with more memory. A 24GB card is identified in the cited guides as a practical tier for larger local coding models; a 16GB card can suit smaller quantized options, with tighter headroom depending on context and other GPU use. Check the exact board’s VRAM and your workload requirements rather than relying on a card family name.
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