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There is no single RAM or GPU-memory requirement for running local AI models. The right amount depends on the model and its quantization, the context length you use, and whether your runtime can keep the workload in GPU memory. As a practical starting point, LM Studio recommends at least 16 GB of system RAM and 4 GB of dedicated GPU memory for Windows, and 16 GB or more of RAM for Apple Silicon Macs. Those are platform recommendations, not guarantees that a particular model will run well.
What RAM and GPU memory do for local AI
System RAM and GPU memory are separate resources on most Windows PCs. System RAM holds data used by the operating system and applications, and can also be used when a model or part of its workload cannot fit in dedicated GPU memory. GPU memory, often called VRAM, is the memory on a discrete graphics card. For fast GPU inference, the practical goal is usually to keep the model and the work needed to generate responses in VRAM.
Model weights are only part of the requirement. The runtime also needs memory for active context and inference overhead. Context is the text the model can consider while responding, including the conversation or document you provide. A longer context can raise memory use even when you have not changed the model file. A model that fits at a shorter context may therefore need to offload work when you increase the context setting.
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If the GPU cannot accommodate the workload, some runtimes may split execution between the GPU and CPU, using system RAM for offloaded portions. That can allow a workload to run, but it can reduce generation speed. More system RAM may help accommodate offloaded work; it does not make CPU computation equivalent in speed to keeping the work on the GPU.
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Start with the model and workload, not a universal RAM number
Before judging a computer by a headline such as “16 GB VRAM,” identify what you plan to run. The same machine can handle one model and context comfortably but struggle with another configuration. Check the selected model’s actual file size, quantization, context setting, and the runtime’s model-format and platform support.
Model and quantization
The model determines the size of its weights, while quantization changes how those weights are represented and can change the memory required. Do not infer fit from a model-family name or parameter count alone: check the specific downloadable file and its published requirements. A “20B” label, for example, does not by itself tell you the full memory needed by a given file, context, and runtime.
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Context length
Decide how much text you need the model to consider at once. Short chats and modest prompts impose a different memory load from long documents or extended conversations. If you expect to use long contexts, include that target in your sizing decision rather than assuming that a model’s weights determine the whole footprint.
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Other workload and runtime needs
Confirm that the runtime supports the model format, your operating system, and your GPU. If your local workload includes image input or other modalities, do not assume that a text-only example establishes its memory needs. Runtime overhead also varies, so a model-file size is not a promise that the same amount of available memory will be sufficient.
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Platform-specific starting points
LM Studio’s official System Requirements page, accessed in October 2026, gives useful baseline guidance for that application. It is not a universal minimum for every local-AI runtime, nor a model-by-model compatibility list.
| Platform | LM Studio guidance | How to interpret it |
|---|---|---|
| Windows | At least 16 GB system RAM and at least 4 GB dedicated VRAM recommended. | A general starting recommendation for LM Studio. It does not establish that every model or context will fit or run quickly. |
| Apple Silicon Mac | 16 GB or more of RAM recommended; Macs with 8 GB may still work with smaller models and modest context. The page lists M1, M2, M3, and M4 Apple Silicon and macOS 14.0 or newer. | Use unified-memory capacity and the runtime’s guidance when sizing an Apple Silicon system; do not add “RAM” and “VRAM” as though they were separate pools on these Macs. |
These figures answer what LM Studio recommends as a general platform baseline. They do not tell you that a Windows PC with 4 GB VRAM can run any particular model, or that 16 GB on a Mac is enough for every context and workload. Verify the specific model and runtime before treating a specification as a compatibility guarantee.
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What the memory trade-off looks like in practice
A Windows Central hands-on test by Managing Editor Richard Devine, published August 25, 2025, illustrates how context can change performance even when the computer is unchanged. The test system used an RTX 5080 with 16 GB VRAM, an Intel Core i7-14700K, and 32 GB DDR5 system RAM. Windows Central described the test as a simple test rather than a scientific benchmark, so use the figures as an example of the trade-off, not a forecast for another PC.
| Test configuration | Reported result | What it illustrates |
|---|---|---|
| DeepSeek-R1 14B, up to 16k context, on the RTX 5080 test system | About 70 tokens per second with full GPU use. | The workload remained on the GPU at the tested context setting. |
| DeepSeek-R1 14B, 32k context, on the same system | About 19.2 tokens per second; CPU offload appeared. | Increasing context changed the memory fit and coincided with a substantial speed reduction in this test. |
| gpt-oss 20B, up to 8k context, on the same system | About 128 tokens per second. | This is a result for that model and tested configuration, not a general 20B-model rate. |
| gpt-oss 20B, 16k context, on the same system | About 50.5 tokens per second as CPU offload appeared. | The tested model’s longer context coincided with offload and lower throughput. |
Windows Central described gpt-oss:20b as 13 GB installed and suggested 16 GB VRAM for models up to that example, with 24 GB providing more room for intensive workloads. Treat those as practical guideposts from that article, not hard compatibility tiers: the article itself cautions that results vary with model, context, runtime, and hardware.
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How to size a PC for the local models you want
- Choose a target workload. Write down the model you intend to use, its exact file or quantization, the context length you need, and whether you will use image input or another modality. Avoid shopping from a model-family label alone.
- Check the runtime’s requirements and support. Confirm that it supports your operating system, GPU, and model format. Use its current platform guidance as a baseline, not as proof that a particular model configuration will fit.
- Check memory fit for the exact configuration. Compare the model file and its documented requirements with the memory available to the runtime. Leave room for context and runtime use rather than assigning all available VRAM to weights.
- Decide whether offload is acceptable. If the workload cannot stay in VRAM, it may still run with GPU/CPU execution and system-RAM offload. Decide whether that slower execution is acceptable for your use before treating “it launches” as a successful hardware fit.
- Compare the whole platform before buying. Consider available VRAM or unified memory, target context, runtime compatibility, upgradeability, and current hardware cost together. On a desktop, verify system-RAM type and motherboard compatibility before buying a memory upgrade. On an Apple Silicon Mac, check whether the chosen configuration provides enough unified memory, since the cited LM Studio guidance treats its RAM as the relevant capacity.
Choosing between more system RAM and more VRAM
If your goal is fast inference on a PC with a discrete GPU, prioritize enough VRAM for the intended model and context, with capacity left for runtime use. More VRAM can make it more feasible to keep a workload GPU-resident. The Windows Central test’s 16 GB and 24 GB guideposts are tied to its cited example and are not universal purchase thresholds.
System RAM still matters: it supports the operating system and other applications, and can be used for work offloaded from the GPU. LM Studio recommends at least 16 GB on Windows, but adding system RAM is not the same as adding GPU memory. It can make room for workloads that rely on CPU/system-RAM offload without guaranteeing the speed of full GPU inference.
On Apple Silicon, the relevant capacity is unified memory rather than two independently additive pools labeled system RAM and VRAM. Follow the runtime’s platform guidance and match it to the model and context you plan to use. LM Studio says its 8 GB Mac users may be able to run smaller models with modest context; that qualification should not be stretched to larger models or long-context use.
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- It loads, but generation is slower than expected: Check whether the runtime is offloading work to the CPU. The Windows Central examples show that offload can appear at a longer context on the same hardware.
- It fits at first, then struggles with a long conversation or document: Reduce the context setting or use a smaller model or different quantization. Context contributes to memory use in addition to weights.
- The model will not load: Verify the exact model file, quantization, runtime support, and available memory rather than relying only on the model’s name. If the runtime supports offload, it may be an option, but it can reduce speed.
- You are deciding whether to upgrade: If the target workload is GPU inference and VRAM is the constraint, more system RAM alone will not turn a discrete GPU with limited VRAM into a higher-VRAM GPU. If system memory itself is constrained, verify that a compatible upgrade is possible before purchasing.
How to read memory claims without overbuying
Memory figures are meaningful only with their conditions. A requirement may be an application’s general system recommendation; a benchmark result may describe one model, context, runtime, and computer. A memory amount that allows one configuration to run does not show that it will run at a preferred speed or support a longer context.
For a buying decision, use the model’s specific file and runtime guidance first, then match memory capacity to the context and performance you expect. The cited platform recommendations can narrow the starting point, and the Windows Central results show why context and offload belong in the decision. Neither source establishes a universal formula that converts model size into a guaranteed RAM or VRAM requirement.
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