There is no single RAM or VRAM minimum for running a local coding model. The amount you need depends on the model and quantization you choose, the context length you want, the inference runtime, and what else is using your computer. Use the model’s file size as a starting point—not as a promise that it will fit in the same amount of memory at runtime.
What determines memory use?
Start with the model’s weights, then allow for the memory required by its context and inference runtime. For GPU-only inference, the available VRAM must accommodate those allocations. A longer context can substantially increase memory use, so a model that fits at a short context may not fit at the context length you want.
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The downloadable file size is not the total inference-memory requirement. It does not, by itself, account for runtime overhead, context, or memory shared with your operating system and other applications. Quantization and runtime also affect the configuration, so compare actual model variants rather than relying on a parameter count alone.
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Use model file sizes as a starting point
Ollama’s Qwen2.5-Coder library lists variants from 0.5B to 32B parameters. The sizes below are the library’s displayed downloadable file sizes, not measured RAM or VRAM requirements: Ollama Qwen2.5-Coder model library.
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| Qwen2.5-Coder variant | Listed model file size | What the figure tells you |
|---|---|---|
| 0.5B | 398 MB | Download size only; runtime and context need additional memory. |
| 3B | 1.9 GB | Download size only; runtime and context need additional memory. |
| 7B | 4.7 GB | Download size only; runtime and context need additional memory. |
| 14B | 9.0 GB | Download size only; runtime and context need additional memory. |
| 32B | 20 GB | Download size only; runtime and context need additional memory. |
These figures help narrow the candidate models, but they cannot tell you whether a particular GPU or computer will run one comfortably. Check the specific quantization and runtime you intend to use, then account for the target context and memory needed by other workloads.
Why context length can change the answer
Context length is the amount of text the model can consider in a request. Coding assistants may need a large context to work with longer files or broader project information, and that context requires memory beyond the weights.
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In its January 23, 2026 guidance for the coding-tool integrations it discusses, Ollama recommends a context length of at least 64,000 tokens. The same article gives an example of approximately 23 GB of VRAM for a specific model at a 64,000-token context. That is a model- and configuration-specific example, not a general minimum for coding models. Ollama’s recommendation applies to those integrations; it does not mean every coding task requires a 64,000-token context. See Ollama’s coding integrations guidance.
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Keeping the model on a discrete GPU
When the model is placed on a discrete GPU, VRAM is the immediate constraint. The GPU needs enough available memory for the model’s runtime allocations and chosen context, not just a capacity equal to the downloaded file size. A “GPU with 16 GB VRAM” is a useful capacity tier to compare, but it is not a universal requirement or guarantee of fit.
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Using the CPU or splitting work between CPU and GPU
CPU inference and mixed CPU/GPU offload can use system RAM, while a mixed setup also uses GPU memory for the work placed there. The amount of system RAM required and the speed tradeoff depend on the particular model and runtime; the cited sources do not establish a universal system-RAM minimum or quantify that performance tradeoff. Check the requirements for your intended configuration rather than applying one RAM figure to all local models.
Choose a configuration before buying memory
- Pick a model and variant. Compare the model family, parameter tier, quantization, and actual file size. For example, Ollama lists Qwen2.5-Coder 7B at 4.7 GB, 14B at 9.0 GB, and 32B at 20 GB as downloadable file sizes—not total memory requirements.
- Set a realistic context target. Decide how much project material the model needs to handle. If you are using an Ollama coding integration covered by its January 23, 2026 guidance, factor in its recommendation of at least 64,000 tokens and consult its model-specific VRAM example.
- Choose where inference will run. For GPU-only inference, compare the allocations with available VRAM. For CPU or mixed inference, check the selected runtime’s system-RAM and offload requirements.
- Leave room for the rest of your computer. The operating system, IDE, browser, and other workloads can use memory too. Do not treat all installed RAM or VRAM as available to the model.
- Verify the exact runtime configuration. Check the current model variant, quantization, context setting, and runtime guidance before choosing hardware. Defaults, variants, and listed file sizes can change.
What can—and cannot—be estimated from published figures
The cited vendor pages give useful model file sizes and a specific long-context VRAM example, but they do not provide a universal formula, a general system-RAM minimum, or comparative GPU performance measurements. As a result, no single RAM or VRAM number can be responsibly presented as sufficient for every local coding model. Size memory against the model, quantization, context, runtime, and competing workloads you actually plan to use.
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