How Much VRAM Do You Need for Local AI?

GPU memory guide

The useful VRAM target comes from a workload, an exact model, a context length, and an offload plan—not a universal 8GB, 12GB, or 24GB rule.

Documentation review updated July 17, 2026. Examples describe documented runtime behavior and are not Kingy.ai benchmark results.

The practical answer

For text generation, enough VRAM means the selected model allocations and the intended context fit on the GPU with usable headroom. For image or video generation, pipeline components, precision, resolution, batch size, and offloading determine the peak. A model can sometimes run with less dedicated VRAM by keeping layers or cache in system RAM, but that changes performance and system-memory demand.

LM Studio recommends at least 4GB of dedicated VRAM for its supported Windows configuration. That is an application baseline, not a claim that 4GB fits every local-AI workload. Its own load estimator exists because model and context choices matter.

Separate “can load” from “runs fully on the GPU.” llama.cpp supports CPU+GPU hybrid inference for models larger than total VRAM, and Ollama reports CPU/GPU placement with ollama ps. Partial offload can avoid a hard failure while remaining much slower than full acceleration.

What competes for VRAM

Model weights

Full GPU offload places the selected weights on the GPU. Quantization can reduce their footprint, but the exact format and backend must be supported.

KV cache and context

Text models store attention state for the active context. Larger context windows require more memory, and some runtimes let the cache stay in RAM instead of VRAM.

Runtime workspaces

Inference kernels need working memory beyond the model file. Flash attention, batch size, parallel requests, and backend implementation can change the peak.

Multimodal components

Vision and audio models may load additional encoders or projector files. A text-only size estimate may therefore understate a multimodal workload.

Image-generation pipeline

Diffusion systems can include a transformer or UNet, text encoders, and a VAE. Resolution and the number of images processed together can raise activation memory.

Other GPU users

Displays, browsers, creative software, games, and additional models also consume GPU memory. Nominal card capacity is not the same as memory available to one process.

Plan by workload

Workload	Primary VRAM drivers	Best preflight evidence	If it does not fit
Short LLM chat	Quantized weights, modest context cache, runtime buffers.	Estimate the exact model with the intended context and GPU-offload setting.	Use a smaller artifact, reduce context, or accept partial CPU offload.
Long-document or coding agent	Weights plus a much larger context or KV cache; possible parallel requests.	Estimate with the real context length rather than the runtime default.	Reduce context, use cache quantization where supported, or choose a smaller model.
Vision-language model	LLM weights, vision encoder or projector, image inputs, and context.	Use a vision-aware estimator and the exact multimodal variant.	Reduce image workload or use a smaller supported multimodal model.
Image generation	Pipeline weights, precision, activations, resolution, batch size, and adapters.	Use the model pipeline’s own documentation and measure peak memory on a representative generation.	Use model, group, or sequential CPU offload; expect a speed tradeoff.
Concurrent local service	Model plus multiple contexts, batches, and request queues.	Load-test the expected concurrency rather than a single prompt.	Lower concurrency, shorten context, or move to a larger GPU or hosted service.

Context length is a configuration, not free capacity

Ollama’s current documentation chooses default context lengths partly from detected VRAM and warns that increasing context length increases memory use. Its defaults are runtime policy, not a universal model requirement. A model advertising 128K or 256K context does not mean every GPU that can load its weights can also run that maximum efficiently.

LM Studio’s estimator accepts context length and GPU offload, and its load API can place the KV cache in GPU memory or CPU RAM. That distinction is important: moving cache out of VRAM can free GPU memory while increasing RAM use and changing performance.

Image generation needs its own calculation

Do not transfer LLM rules directly to diffusion models. Hugging Face’s Diffusers documentation notes that modern image and video pipelines can exceed common GPU memory and provides several offloading strategies. Model offload moves whole components between CPU and GPU; sequential offload can save more memory but is substantially slower because components move repeatedly.

Resolution and batch size can change peak activation memory even when model weights stay the same. Evaluate the exact pipeline, precision, resolution, number of images, adapters, and control models you plan to use.

A defensible VRAM check

Confirm that the runtime supports the exact GPU family, driver, and operating system.
Select the exact model artifact, quantization or precision, and multimodal components.
Set the context length, resolution, batch size, and concurrency you expect to use.
Run the runtime’s estimator. In LM Studio, lms load --estimate-only honors context and GPU-offload settings.
Load the model and inspect placement. Ollama’s ollama ps distinguishes full GPU, full CPU, and split placement.
Run a representative task and measure peak VRAM, RAM, speed, and stability before treating the setup as compatible.

What to prioritize when buying

Choose from the workloads backward. A buyer focused on short, small-model chat has a different requirement from one running long-context coding agents or high-resolution image pipelines. Check the runtime’s supported GPU list, then estimate the exact model and settings. More VRAM increases the range of workloads that can remain fully accelerated, but capacity alone does not establish software support or speed.

On Apple Silicon, use unified-memory guidance rather than treating the GPU as if it had a separate VRAM pool. On a Windows or Linux machine with a discrete GPU, plan system RAM as well as VRAM because partial offload and non-GPU allocations may use both.

VRAM for local AI FAQ

Is 8GB of VRAM enough?

It may be enough for a particular quantized model or optimized image pipeline, but there is no general yes. Estimate the exact artifact, context, resolution, and offload settings.

Can system RAM replace VRAM?

Some runtimes can offload layers or cache to RAM, allowing work that does not fully fit on a discrete GPU. The tradeoff can be much lower speed and higher system-memory demand.

Does a larger advertised context require more VRAM?

Using a larger context requires more memory, but the allocation may reside in VRAM, RAM, or both depending on the runtime and settings.