There is no single RAM or VRAM requirement for running a local AI model. The model’s weight file is only the starting point: context length and the inference runtime also use memory. Check the actual model file and format, then allow headroom for the workload; a model that exceeds your GPU’s VRAM may still run through CPU-and-GPU offloading, but potentially at a different speed.
Why a model’s file size is not its full memory requirement
Model weights are the stored parameters the runtime loads to generate responses. Quantization changes how much space those weights take: lower-precision formats can make a model substantially smaller, but the file size alone does not tell you how much total memory a particular run will need.
The llama.cpp project says, “As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them.” Its examples show how much the listed Llama 3.1 model sizes change with Q4_K_M quantization:
| Llama 3.1 model | Original size | Q4_K_M size |
|---|---|---|
| 8B | 32.1 GB | 4.9 GB |
| 70B | 280.9 GB | 43.1 GB |
| 405B | 1,625.1 GB | 249.1 GB |
These are model-size figures in the llama.cpp quantization documentation, not universal minimums for total RAM or VRAM. The runtime and the amount of context being processed affect the full memory budget.
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What VRAM and system RAM do
VRAM: the GPU’s memory pool
When model weights and other inference data fit in GPU VRAM, the GPU can handle the work without relying on system RAM for those allocations. Whether a particular model and workload fit depends on the actual format, context, and runtime—not parameter count alone.
System RAM: loading and CPU-side work
System RAM is used to load a model and can also come into play when inference uses the CPU or when some of the workload cannot stay in VRAM. llama.cpp supports hybrid CPU-and-GPU inference, so a model larger than the available VRAM can sometimes run with parts handled outside the GPU. Feasibility and speed depend on the setup.
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Why context length changes the memory budget
Context is the text the model can take into account during a run. Processing more context requires memory for the context and its associated KV cache, in addition to the model weights. That means a setup that works with a shorter context may use more system RAM or CPU resources at a larger one.
A Windows Central hardware author reported about 70 tokens per second for DeepSeek-R1 14B on an RTX 5080 at a stated context setting up to 16k, then 19 tokens per second after a larger context led to CPU and RAM involvement. This is a report from that author’s setup, not a controlled, generally applicable performance benchmark or a capacity rule. The article also identifies the RTX 3090 as having 24 GB of VRAM. Read the Windows Central setup report.
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How to estimate memory for your intended run
- Choose the exact model and format. Find the model file you plan to use and check its listed size. Parameter count by itself is not enough because quantization can change the weight size substantially.
- Set the context you actually need. A larger context adds memory demand beyond the weights. If you plan to serve multiple requests at once, include concurrency in your estimate; the available figures do not establish a universal allowance for it.
- Compare the workload with VRAM. If your goal is GPU inference, consider whether the full run—not merely the model file—can fit in the GPU’s available memory.
- Check the runtime’s offloading support. If the run exceeds VRAM, supported CPU/GPU hybrid inference may let you run it using system RAM, but performance can change substantially.
- Choose a quantization with the trade-off in mind. Smaller files can make a model easier to load, but the smallest available file is not automatically the best choice. llama.cpp documents multiple quantization levels and format-specific measurements; results depend on the documented test conditions.
For a real hardware decision, compare the model family and parameter count, exact weight format and file size, context length, concurrency, available VRAM and RAM, runtime offloading behavior, and acceptable speed and quality. The cited material does not establish one exact capacity that works for every model at a given parameter count.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available figures can—and cannot—tell you
The Llama 3.1 examples provide concrete model sizes for original and Q4_K_M files, while llama.cpp documents hybrid CPU/GPU inference. Together, they help explain why model format and offloading matter. They do not provide universal minimum RAM or VRAM requirements across models, contexts, and runtimes, nor a controlled comparison of CPU-offload performance. Treat the Windows Central token rates as results from the author’s stated setup, not a prediction for another computer.
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