How Much VRAM Do You Need for Local AI?

Why VRAM matters

When local inference uses a discrete GPU, VRAM holds model weights and working data close to the GPU. If the model does not fit, the runtime may offload part of it to system RAM or the CPU, reduce the amount placed on the GPU, or fail entirely. Offloading can make a previously impossible model run, but it can also reduce speed dramatically.

The required VRAM is not a fixed number for a model family. Quantization level, context length, runtime implementation and architecture change the footprint. Image-generation and multimodal models have different memory behaviour from text-only language models. Treat published examples as starting points and validate on the exact runtime you intend to use.

Integrated graphics and shared memory

Many laptops do not have a large dedicated VRAM pool. Integrated GPUs use system memory, so RAM capacity and memory bandwidth become especially important. A device may advertise a large amount of “shared GPU memory,” but that does not mean the GPU can use it with the same performance as dedicated high-bandwidth VRAM.

For browser Local AI, WebGPU can make capable integrated graphics useful for small models. EONAPP’s adaptive approach should test the actual device rather than trusting a marketing label. A weak phone, an integrated laptop GPU and a gaming GPU are different classes even when each technically exposes GPU acceleration.

What 4 GB, 8 GB and 12–16 GB feel like

Four gigabytes of dedicated VRAM is best treated as an entry tier for small models and experiments. Eight gigabytes is considerably more flexible and can support a broader range of quantized text models, but large context or heavier multimodal work can still push past the limit. Twelve to sixteen gigabytes opens more serious local-model options and gives useful breathing room for creators and developers.

These tiers are intentionally qualitative because a single number cannot guarantee a specific model. Before buying hardware for one workload, check the actual model weight format and runtime memory guidance, then leave headroom. If the goal includes local image generation, video or large multimodal models, plan separately; those workloads can be far more demanding than small private chat.

VRAM versus compute speed

Capacity answers “can it fit?” while GPU compute and bandwidth help answer “will it be fast enough?” A high-capacity but slow GPU can load a model and still feel frustrating. Conversely, a fast GPU with too little VRAM may spend time offloading. Laptop GPUs also vary by power limit, so the same product name can behave differently in different chassis.

Use tokens per second, first-token latency, power/thermal stability and context size as real performance measures. For mobile/browser AI, stability is often more important than peak benchmark speed because the browser must coexist with the rest of the device.

Choose for the workload, not the badge

If your main goal is private note rewriting and short chat, you may not need a large discrete GPU at all. If you expect sustained coding, long documents, image generation or larger models, more VRAM becomes valuable. Combine VRAM with system RAM, storage, upgradeability and power limits. The best machine is the one that can run your actual workload reliably, not the one with the most impressive specification in isolation.