# How Much VRAM Do I Need for a Local LLM?

- Canonical: https://cloudzat.com/how-much-vram-for-local-llm/
- Published: 2026-08-03
- Updated: 2026-08-03
- Author: Kayla Idayi
- Site: https://cloudzat.com/
- LLM index: https://cloudzat.com/llms.txt

## Content

[Home](https://cloudzat.com/)/[GPU & Local AI](https://cloudzat.com/gpu-price-tracker-for-local-ai/)/How Much VRAM Do I Need for a Local LLM?

Cloudzat GPU & Local AI Hardware Intelligence

# How Much VRAM Do I Need for a Local LLM?

Model parameters are only the starting point. Quantized weights, context cache, runtime workspace, concurrent users and additional loaded models all consume GPU memory. This calculator turns those inputs into a planning range.

## Calculate local LLM VRAM

Estimate model weights, context cache and runtime headroom.

## Quantization changes the weight footprint

Lower-bit quantization reduces model size and can make larger models practical on consumer GPUs. The trade-off can include quality loss, runtime restrictions or reduced speed depending on the format.

## Long context consumes additional memory

The key-value cache grows with context length and active sequences. A model that fits at 4K context may become tight at 32K or with several simultaneous users.

## Minimum is not the same as comfortable

A model that barely fits can fail when the runtime reserves workspace or another application uses the GPU. Comfortable headroom helps prevent out-of-memory errors.

## CPU offload changes the system requirement

Moving layers to system memory can reduce VRAM pressure, but it increases RAM needs and usually reduces speed. It should be planned deliberately rather than treated as free capacity.

## Current GPU catalogue

Current Amazon offers identified by exact GPU model. Use the calculator above for workload-specific ranking.

| GPU offer | VRAM | Stack | Power | Condition | Price | Price/GB | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Intel Arc A770 16GBGUNNIR Intel Arc A770 Photon 8GB OC GDDR6 2400MHz Triple Fan Graphics Card | 16GBGDDR6 | oneAPI / DirectML | 225W600W PSU | New | Price Options | — | Buy on Amazon |
| Intel Arc B580WEELIAO GUNNIR Intel Arc B580 Index 12GB GDDR6 2670MHz Dual Fan Graphics Card (192-bit, PCI-Express 4.0 x8, HDMI/DisplayPort, Supports 4k) | 12GBGDDR6 | oneAPI / DirectML | 190W600W PSU | New | Price Options | — | Buy on Amazon |
| Intel Arc B580TFGAJKKKKK Intel Arc B Series Intel Arc B580 Limited Edition Graphics Video Card | 12GBGDDR6 | oneAPI / DirectML | 190W600W PSU | New | Price Options | — | Buy on Amazon |
| Intel Arc B580ASRock Intel Arc B580 Challenger 12GB OC & Challenger CL-650G 650W Gold PSU \| Modern Gaming & Streaming Bundle | 12GBGDDR6 | oneAPI / DirectML | 190W600W PSU | New | Price Options | — | Buy on Amazon |
| Intel Arc B580ASRock Intel Arc B580 Challenger 12GB OC & PRO-650G 650W 80+ Gold PSU \| Efficient 1440p Gaming Bundle | 12GBGDDR6 | oneAPI / DirectML | 190W600W PSU | New | Price Options | — | Buy on Amazon |
| Radeon RX 7800 XTSapphire 11330-01-20G Nitro+ AMD Radeon RX 7800 XT Gaming Graphics Card with 16GB GDDR6, AMD RDNA 3 | 16GBGDDR6 | ROCm / DirectML | 263W700W PSU | New | Price Options | — | Buy on Amazon |
| Radeon RX 7800 XTPowerColor Fighter AMD Radeon RX 7800 XT 16GB GDDR6 Graphics Card | 16GBGDDR6 | ROCm / DirectML | 263W700W PSU | New | Price Options | — | Buy on Amazon |
| Radeon RX 7800 XTSapphire 11330-03-20G Pure AMD Radeon RX 7800 XT Gaming Graphics Card with 16GB GDDR6, AMD RDNA 3 | 16GBGDDR6 | ROCm / DirectML | 263W700W PSU | New | Price Options | — | Buy on Amazon |

Amazon prices and images are refreshed on a 24-hour cycle when available. A verified listing without a current featured numeric price is shown as **Price Options**; check Amazon for the current offer.

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## Frequently asked questions

 How much VRAM does a 7B Q4 model need?

The weights are often around 4GB to 5GB, but context cache and runtime overhead mean a comfortable GPU target is higher.

 Why do two runtimes report different memory use?

File format, quantization implementation, cache precision, kernels and workspace allocation can differ between runtimes.

 Does more context always improve an LLM?

No. Longer context increases memory and processing cost, and model quality may not improve for every task.

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