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There is no single RAM or storage minimum for running a local AI model for writing. The amount of working memory you need depends on the model, its quantization, the runtime, the context length, and what else is running on your computer. Storage holds the model files; it does not replace the RAM or GPU memory needed while a model runs.
RAM and storage do different jobs
RAM is working memory used by the operating system, writing apps, and model runtime. On Apple silicon, the system and model share unified memory. A GPU may also use dedicated video memory (VRAM); the amount required depends on how the model is run.
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Storage is where downloaded model files live. For example, llama.cpp supports downloading local models in GGUF format. A model’s download size is not an exact measure of how much memory it will need while running: runtime requirements also depend on configuration and workload.
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Model and quantization
Start by choosing the model and its quantization, rather than relying on a universal RAM figure. Quantization reduces model memory use, and llama.cpp supports quantization levels from 1.5-bit through 8-bit. A more compact quantized version may be easier to fit, but the model and its configuration still matter. See llama.cpp documentation.
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Context length and runtime cache
The amount of text the model can consider at once—the context length—affects the workload. Runtime cache also uses memory. Ollama says cache reuse can reduce memory utilization, but does not give a general numerical allowance for cache, so leave headroom rather than treating a model’s file size as the full requirement. Ollama’s March 30, 2026 preview describes its cache improvements.
Other work on the computer
Compare the memory available after accounting for your operating system and writing applications, not just the computer’s advertised total. On Apple silicon, unified memory serves both system and model workloads; a model may load while leaving little capacity for other apps.
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A specific current recommendation—not a universal minimum
In its March 30, 2026 preview, Ollama advised having more than 32 GB of unified memory for its featured Qwen3.5-35B-A3B configuration. That is a recommendation for that configuration and workflow, not a general minimum for local writing models. The same preview describes an MLX-based Apple silicon workflow. Read Ollama’s preview.
How to estimate storage
Storage needs depend on the model files and variants you choose to keep. Check the selected model’s actual download size, then allow room for additional models or quantizations if you plan to try more than one. The llama.cpp documentation describes downloading local GGUF files but does not specify a required drive capacity. An external SSD can provide extra space for model files; it will not add RAM or VRAM.
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Can you run a model without enough GPU VRAM?
Not necessarily. llama.cpp supports CPU/GPU hybrid inference when a model exceeds total VRAM capacity, so dedicated GPU memory is not the only possible route. The documentation does not establish a particular speed for writing workloads, however. A model that technically loads may not respond at a speed you find usable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to choose a computer
- Pick a model and quantization. Look up the actual configuration you intend to use; requirements vary between models and quantizations.
- Check the runtime. Confirm that your chosen inference software supports the model and hardware combination.
- Compare available working memory. Account for the operating system and writing apps, plus context length and runtime cache.
- Decide what speed is usable. CPU/GPU hybrid inference may make a model runnable, but the cited documentation does not promise a particular writing speed.
- Check disk space separately. Use the selected model’s actual download size and include room for any other files you want to keep.
The available official sources do not provide directly comparable benchmarks for writing workloads or a universal RAM or storage sizing table. Treat any single hardware number as specific to the model, runtime, and workload it describes.
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