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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11There is no single RAM or VRAM minimum for running a local coding model. Start with the specific model and quantization you want, then account for its context length and the inference runtime: the model file size is only a starting point, not a total memory requirement. For example, Ollama lists its Qwen2.5-Coder 7B model at 4.7 GB and 14B at 9.0 GB, but those are downloadable file sizes—not the VRAM or system RAM needed to run them.
What determines the memory a local coding model needs?
Memory needs depend on more than the model’s parameter count. The relevant factors are the model and its quantization, how much context you want it to handle, the inference runtime, and what else is using the computer.
A useful way to think about the total is: model weights, plus memory for the active context, plus runtime overhead. The exact amount varies by configuration, so a download size cannot be treated as a hardware requirement.
Model weights and quantization
Quantization changes how model weights are represented and can affect the model file’s size and memory use. Compare the actual file for the version you intend to run rather than relying only on a model’s parameter tier. Ollama’s Qwen2.5-Coder library lists multiple model sizes and downloadable file sizes, but those files do not establish total inference memory.
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Context length
The context window is the amount of text the model can consider in a session, including prompts and code. Longer context can require additional memory beyond the weights. A short interaction and a large codebase task may therefore have different memory demands even when they use the same model.
Runtime and other workloads
The inference runtime affects how memory is allocated. So do other applications: the operating system, IDE, browser, and concurrent workloads can use memory that would otherwise be available to the model. Leave capacity beyond the model file size rather than planning for a configuration that consumes every available gigabyte.
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What do published model sizes tell you?
Ollama’s Qwen2.5-Coder library lists these variants and downloadable file sizes. The sizes are useful for comparing model files, but do not guarantee that a system with that amount of RAM or VRAM can run the model.
| Qwen2.5-Coder variant | Listed model file size | How to interpret it |
|---|---|---|
| 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 values are the sizes displayed in the Ollama Qwen2.5-Coder library, accessed in 2026. Other quantizations or model versions may have different file sizes.
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How much VRAM should you plan for?
For a model kept on a discrete GPU, available VRAM is the immediate constraint: it must accommodate the model and the additional allocations for context and the runtime. A card’s stated VRAM capacity is therefore not the same thing as the largest model file it can load.
Ollama’s January 23, 2026 article on coding integrations recommends a context length of at least 64,000 tokens for the integrations discussed there. It gives an example of approximately 23 GB of VRAM for a specific model at that context length. That is a model- and configuration-specific example, not a general VRAM minimum for coding models, and the recommendation does not mean every coding task needs a 64,000-token context.
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When comparing GPUs, match available VRAM to your chosen model file, desired context, runtime, and memory already in use. A GPU with 16 GB of VRAM is one capacity tier to compare—not a universal requirement or a guarantee that a particular model and context will fit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How does system RAM fit in?
System RAM matters when inference runs on the CPU or the runtime places some model work in system memory. Mixed CPU/GPU offload can also draw on both pools. In those configurations, available system RAM can affect whether a model can be loaded, but it does not make system RAM interchangeable with a discrete GPU’s VRAM in every setup.
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There is no universal system-RAM minimum established for CPU inference or offloading across models and runtimes. Check the requirements for the exact model and runtime, and allow room for the operating system and other applications. The sources cited here do not quantify a general RAM floor or the speed tradeoff of offloading.
How to choose memory for your workload
- Choose the model first. Identify its family, parameter tier, and specific version rather than starting with a generic memory target.
- Check the file you will download. Record its quantization and actual size. Treat that figure as the weight footprint, not the complete runtime requirement.
- Decide how much context you need. Larger codebase tasks may call for a longer context and more memory. Use the context settings and guidance for your chosen runtime and coding workflow.
- Identify where inference will run. For GPU inference, compare the workload with available VRAM. For CPU inference or offloading, account for available system RAM as well.
- Allow headroom. Include runtime allocations and memory used by the operating system, IDE, browser, and other workloads.
- Verify the exact combination. Check the current model and runtime requirements before buying hardware; file sizes, quantization choices, and context defaults can change.
Ollama’s coding-integration guidance is available at ollama.com/blog/launch. Its 64,000-token recommendation applies to the integrations covered in that article, not to every coding model or use case.
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