The cheapest GPU to run Llama locallyis more accessible than many newcomers expect. Llama 3.1 8B is the model most people mean when they refer to "running Llama". It needs only about 5 GB of VRAM at 4-bit quantization, which places it within reach of any modern 8 GB graphics card. The practical question is therefore not whether a budget card can run Llama but which inexpensive card offers the best balance of speed and room to grow. This guide compares the leading budget options using live data from the same engine as the calculator.
What running Llama locally actually requires
A model's memory requirement is dominated by its weights. At 4-bit precision, the rule of thumb is roughly 0.6 GB of VRAM per billion parameters, so Llama 3.1 8B needs about 5 GB plus a small allowance for context and overhead. Any card with 8 GB of VRAM or more runs it entirely on the GPU at good speed. The larger Llama models change the calculation. The 70-billion-parameter Llama 3.3 needs roughly 42 GB and does not fit on any single budget card, a point returned to below.
| Card | VRAM | Biggest on-GPU | Llama 8B speed |
|---|---|---|---|
| RTX 3060 | 12 GB | ~45 tok/s | |
| RTX 4060 | 8 GB | ~34 tok/s | |
| Arc B580 | 12 GB | ~57 tok/s | |
| Radeon RX 7600 | 8 GB | ~36 tok/s | |
| RTX 4060 Ti 16GB | 16 GB | ~36 tok/s |
Every card above runs Llama 3.1 8B comfortably. The figures that separate them are how much VRAM is left for larger models and how quickly each decodes, which a side-by-side comparison of any two cards lays out directly. Speed is reported for a standard 8B model at 4-bit so the cards can be compared on equal terms.
The value pick: a 12 GB card
For most buyers, the best value is not the absolute cheapest card but a 12 GB one such as the RTX 3060 12 GB or the newer Intel Arc B580. The extra memory over an 8 GB card costs little but lifts the ceiling from 8-billion-parameter models to the 13-to-14-billion-parameter range, which is where local models become noticeably more capable. The RTX 3060 12 GB in particular has remained a long-standing favourite for local AI precisely because it pairs a low price with enough VRAM to grow into.
The cheapest route: 8 GB
If the budget is tighter, an 8 GB card such as the RTX 4060 or the Radeon RX 7600 runs Llama 3.1 8B well and represents the lowest-cost entry into local AI. The trade-off is headroom. These cards are confined to the 8-billion-parameter class, and stepping up to a 13-billion-parameter model means offloading to system RAM at much lower speed. As an entry point they are entirely serviceable. As a long-term home for local AI they are easy to outgrow. Note also that setup is smoother on NVIDIA than AMD today. See whether you need an NVIDIA GPU for the full comparison.
What about the larger Llama models?
Running the 70-billion-parameter Llama 3.3 is a different proposition entirely. At roughly 42 GB it exceeds every budget card and indeed every single consumer card short of the 32 GB flagships. It requires either two 24 GB GPUs or slow offload to system RAM. For the vast majority of users the sensible target is Llama 3.1 8B, or a 13-to-14-billion-parameter model on a 12 GB card, and the budget options above serve it well.
The bottom line
The cheapest practical way to run Llama locally is an 8 GB card such as the RTX 4060 or RX 7600. The best value is a 12 GB card such as the RTX 3060 12 GB, which adds room to grow for little extra cost. For a fuller ranking across budgets see the best GPUs for local LLMs, and for the memory requirements behind these recommendations see how much VRAM you need to run an LLM. Whichever card is under consideration, confirm its exact capability in the calculator before buying.