Cloudzat GPU & Local AI Hardware Intelligence
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 |
16GB GDDR6 |
oneAPI / DirectML | 225W 600W PSU |
New | Price Options | — | Buy on Amazon |
 Intel Arc B580acer Nitro Intel Arc B580 Overclocking Graphics Card | 12GB GDDR6 | Xe2 20 Cores | Dual Fan Frostblade Cooling System | Graphics Clock 2.74 GHz | 1 x HDMI 2.1 Supporting HDMI 4K & 3 x DisplayPort 2.1 |
12GB GDDR6 |
oneAPI / DirectML | 190W 600W 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 |
12GB GDDR6 |
oneAPI / DirectML | 190W 600W 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 |
16GB GDDR6 |
ROCm / DirectML | 263W 700W PSU |
New | Price Options | — | Buy on Amazon |
 Radeon RX 7800 XTPowerColor Fighter AMD Radeon RX 7800 XT 16GB GDDR6 Graphics Card |
16GB GDDR6 |
ROCm / DirectML | 263W 700W PSU |
New | Price Options | — | Buy on Amazon |
 Radeon RX 7800 XTPowerColor Twin Fan AMD Radeon RX 7800 XT 16GB GDDR6 |
16GB GDDR6 |
ROCm / DirectML | 263W 700W PSU |
New | Price Options | — | Buy on Amazon |
 Radeon RX 7800 XTXFX Speedster SWFT210 Radeon RX 7800XT Gaming Graphics Card with 16GB GDDR6 HDMI 3xDP, AMD RDNA 3 RX-78TSWFTFA |
16GB GDDR6 |
ROCm / DirectML | 263W 700W PSU |
New | Price Options | — | Buy on Amazon |
 Radeon RX 7900 XTXFX Radeon RX 7900XT Gaming Graphics Card with 20GB GDDR6, AMD RDNA 3 RX-79TMBABF9 |
20GB GDDR6 |
ROCm / DirectML | 315W 750W PSU |
New | Price Options | — | Buy on Amazon |
Amazon prices and images are refreshed on a 24-hour cycle when available. A matching listing without a current featured numeric price is shown as Price Options; check Amazon for the current offer.
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.