PC Slower Than It Used to Be?
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThere is no single RAM or VRAM requirement for running a local AI model. Start with the actual model file and its quantization, then allow additional memory for the context window and inference runtime. VRAM is the GPU’s memory; system RAM can hold model data too, and some software can split work between CPU and GPU. A model file fitting on disk—or even in memory—does not guarantee it will fit entirely in VRAM or run at a speed you find acceptable.
Why model size is not the whole memory requirement
A model’s parameter count is not a reliable memory figure by itself. The weight format matters: quantization stores weights using fewer bits and can reduce the model file substantially. The file size is a useful starting point, but inference also needs memory for the context and runtime.
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The llama.cpp project documents the following Llama 3.1 model sizes. These are model-size figures, not guarantees of total RAM or VRAM needed by every setup.
| 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 sizes are listed in the llama.cpp quantization documentation. The project notes that models are loaded into memory and therefore require sufficient RAM, as well as disk space to store them. A smaller quantized file can make a model feasible on more hardware, but quantization levels involve trade-offs; the smallest file is not automatically the right choice for every user.
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What VRAM and system RAM do
VRAM: memory on the GPU
VRAM is the GPU’s own memory pool. If the weights and other inference needs fit there, the GPU can handle the model without relying on system RAM for those parts. For a GPU-focused setup, compare available VRAM against more than just the model file size: context and runtime also need headroom.
System RAM: memory for CPU and hybrid inference
System RAM is used when inference runs on the CPU and can also help when a supported runtime divides work between CPU and GPU. llama.cpp supports hybrid CPU/GPU inference, so a model larger than the GPU’s VRAM may still be usable without fitting wholly in VRAM. Whether that works—and how fast it feels—depends on the software, model, and machine.
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Context length adds to memory use
The context window is the text the model can consider while responding. Longer context can require additional memory for the key-value (KV) cache, beyond the model weights. As a result, a setup that handles a model at a shorter context may run out of GPU memory or start relying on system RAM and the CPU when the context grows.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsOne Windows Central hardware report illustrates why the context setting matters: its 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. Those are results from that author’s particular setup, not a controlled benchmark or a prediction for other computers. The same article identifies the RTX 3090 as a 24 GB VRAM card; that example does not establish a universal capacity target.
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For serving a model to multiple users, concurrent requests can add further memory demands. The available figures here do not establish a universal KV-cache or concurrency allowance, so check the requirements and behavior of the specific runtime rather than treating one context example as a general threshold.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to estimate memory for your setup
- Choose the model and exact file. Identify its parameter size and weight format, then check the actual file size. Do not infer memory needs from the parameter count alone.
- Set the context you intend to use. Include the prompt and response workload you expect; a larger context can increase memory use.
- Check the runtime’s placement options. Find out whether it can keep the model on the GPU, run it on the CPU, or split it between them.
- Compare with available memory and leave headroom. For a GPU-first setup, compare the full workload—not only the model file—with available VRAM. For CPU or hybrid inference, consider system RAM as well.
- Decide what trade-off is acceptable. Quantization, CPU offload, and a shorter context may change what fits, but can affect output quality, speed, or usability. Verify with the model and runtime you plan to use.
There is no evidence-based universal rule that 8 GB is enough or that every local model requires 24 GB of VRAM. A sound capacity recommendation needs the specific model, quantization, context length, runtime, and—for serving—concurrent workload.
What the published figures can and cannot tell you
The llama.cpp sizes are documented examples for particular Llama 3.1 formats, not a cross-runtime memory calculator. The Windows Central token rates describe one author’s hardware and settings. Together, these sources show why file size, VRAM, system RAM, context, and offloading must be considered separately; they do not supply a minimum RAM or VRAM figure for every model at a given parameter count.
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