There is no single RAM requirement for running a local AI model. The amount depends on the model and its quantization, the context length and number of simultaneous requests, the runtime, and whether inference uses system RAM, GPU memory, or both. Start with the model file’s size, but leave additional memory for the runtime, context cache, operating system, and other apps.
Start with the model’s actual file size
Model parameter count alone does not tell you how much memory you need. The precision or quantization used for the weights can change the file size substantially. For example, the llama.cpp project’s quantization documentation, observed in 2026, lists these Llama 3.1 file sizes. The page does not state a publication date, and these are examples for the listed models and formats—not universal sizes for every model of the same parameter count.
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| Model | Original file size | Q4_K_M file size |
|---|---|---|
| Llama 3.1 8B | 32.1 GB (llama.cpp documentation) | 4.9 GB (llama.cpp documentation) |
| Llama 3.1 70B | 280.9 GB (llama.cpp documentation) | 43.1 GB (llama.cpp documentation) |
| Llama 3.1 405B | 1,625.1 GB (llama.cpp documentation) | 249.1 GB (llama.cpp documentation) |
The figures make the quantization tradeoff clear: Q4_K_M versions are much smaller than the original files in these examples. Quantization reduces memory use, but it is a size-and-quality tradeoff; the effect on output quality depends on the model and task. A smaller model file is not, by itself, proof that a computer with exactly that much RAM can run it well. The llama.cpp documentation notes that models are loaded into memory and that users need sufficient RAM to load them.
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Context length and the KV cache
A longer prompt or context requires more memory beyond the model weights. Ollama documents Flash Attention as a way to significantly reduce memory use as context grows when supported. It also documents quantizing the K/V cache as another way to save memory. These options and their tradeoffs depend on the runtime; do not assume they are available or behave identically in every local-AI application. See Ollama’s context-length documentation.
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Simultaneous requests
Serving multiple requests at once increases the context-memory budget. Ollama says its RAM requirement scales with OLLAMA_NUM_PARALLEL multiplied by OLLAMA_CONTEXT_LENGTH. In its example, four parallel requests at a 2K context length amount to an 8K total context allocation. That example describes Ollama’s allocation behavior, not a universal formula for every runtime.
Runtime and other applications
Do not plan to give all installed memory to the model. The operating system, inference runtime, context cache, and other active applications also need working room. The cited documentation does not establish a universal reserve amount, so the practical budget depends on what else is running and how the chosen runtime handles memory.
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Know whether the model uses RAM, VRAM, or both
System RAM and GPU VRAM are different pools, and the amount of each matters according to where inference runs. In Ollama, the ollama ps command shows whether a model is loaded on the CPU, GPU, or across both. llama.cpp also documents CPU-and-GPU hybrid inference for models that exceed available VRAM. Unified-memory computers further complicate simple RAM-versus-VRAM comparisons, so any specific capacity estimate needs to name the hardware and runtime. See Ollama’s FAQ and the llama.cpp project documentation.
Estimate your own setup
- Choose the model and format. Check the actual file size for the model and quantization you intend to run; do not estimate from parameter count alone.
- Set the workload. Account for the context length you need and whether you will handle one request or several in parallel.
- Check memory placement. Find out whether your runtime uses system RAM, GPU VRAM, or a split, and whether it supports relevant cache or attention options.
- Allow room for the rest of the computer. Include the operating system, runtime, and other applications rather than treating the model file size as a complete system requirement.
- Check upgrade compatibility before buying memory. Confirm that your computer is upgradeable and verify its supported memory type and maximum capacity in the manufacturer’s specifications.
This approach gives you a workload-specific estimate rather than a universal threshold. The cited documentation does not define a minimum or “comfortable” RAM tier that guarantees a particular model will run at a particular speed.
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