There is no single RAM or VRAM minimum for every local large language model (LLM). The right amount depends on the exact model file and quantization, how much context it must handle, where inference runs, and whether you load other models or serve multiple requests. Start with the model and workload you want to run, then compare their memory needs with the capacity your system can actually make available.
Start with your model and workload, not a universal number
Local LLM memory use is shaped by several choices that interact. A model’s parameter count alone cannot tell you precisely how much memory it needs: the downloadable file’s format, runtime, context length, and execution mode all matter. A model file’s size is a useful starting point, but it is not the whole system requirement.
- Model and quantization: Identify the exact model variant and quantized file you plan to run. Quantization can reduce the memory needed for model weights, but lower-precision formats can involve quality trade-offs.
- Context length: Longer conversations or prompts require more memory for the key/value (KV) cache.
- Execution mode: CPU inference uses system RAM; GPU inference uses available VRAM for work placed on the GPU. Some runtimes can divide work between CPU and GPU.
- Other activity: The operating system, applications, runtime, other loaded models, and simultaneous requests also consume memory.
What the published system recommendations tell you
LM Studio’s system-requirements page recommends 16GB or more of system RAM for Apple Silicon macOS. It also says, “You may still be able to use LM Studio on 8GB Macs, but stick to smaller models and modest context sizes.” For Windows, LM Studio recommends at least 16GB of system RAM and at least 4GB of dedicated VRAM. These are broad recommendations from the software vendor, not guarantees that a particular model, context size, or workload will fit. LM Studio system requirements
How to estimate memory for your setup
- Choose the exact model file. Check the size and quantization of the model variant you intend to download. llama.cpp supports quantization formats from 1.5-bit through 8-bit integer quantization; formats with lower memory requirements may involve quality trade-offs. Do not treat a parameter-count conversion as an exact memory requirement. llama.cpp documentation
- Set a realistic context target. Decide how much prompt and conversation history you need the model to handle. A longer context increases KV-cache memory use. Ollama documents Flash Attention and quantized KV caches as options to reduce that use; lower-bit cache settings can trade precision for memory savings. Ollama FAQ
- Choose CPU, GPU, or split execution. For CPU inference, system RAM is the relevant pool. GPU inference needs enough available VRAM for the portion of the model handled by the GPU. Ollama considers available VRAM when loading models. llama.cpp can split execution between CPU and GPU when a model exceeds VRAM, but this changes the performance profile. Ollama FAQ · llama.cpp documentation
- Account for parallel requests and other software. Leave capacity for the operating system, applications, and runtime overhead. Ollama says required RAM scales with
OLLAMA_NUM_PARALLEL × OLLAMA_CONTEXT_LENGTH, so increasing parallel requests or context length increases memory requirements. Ollama FAQ
Understand what happens when a model does not fit
Running on the CPU
CPU inference draws on system memory. Having enough RAM for the model’s weights is not sufficient by itself if the context cache and the rest of the workload push total use beyond available capacity.
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Running on the GPU
GPU inference uses available VRAM for the model work placed on the GPU. The memory needed varies with the selected model file, context, and runtime; a GPU’s advertised VRAM capacity does not by itself establish that a particular workload will fit.
Splitting work between CPU and GPU
With llama.cpp, CPU/GPU splitting can make it possible to run a model larger than the available VRAM by keeping some work on the CPU. It changes where computation happens, so do not assume it will perform like a workload that fits wholly in VRAM. The documentation supports this as an execution option, not a universal speed guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare hardware using the same workload
When assessing two systems, hold the model variant, quantization, context target, runtime, and number of simultaneous requests constant. Then compare whether the model fits in available memory, whether execution is on CPU, GPU, or split, and what platform and backend support the runtime provides. The cited documentation does not establish a controlled cross-platform benchmark, so it cannot support a general claim that a particular RAM or VRAM capacity will deliver a specific speed.
For a concrete recommendation, identify the exact model file, runtime, context target, and hardware first. Then verify the file size and the runtime’s current memory behavior and requirements; software support and defaults may change.
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