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For most local-AI users, 8GB of VRAM is an entry point, 12–16GB is the practical mainstream range, 24GB is the enthusiast sweet spot, and 32GB or more provides useful headroom for larger models and longer contexts. Local video generation is a different class: representative workflows documented by NVIDIA peak at approximately 80GB, 100GB, and 120GB. Those figures include more than model weights. You must also budget for the operating system, desktop, KV cache, activations and any other GPU applications.
What does VRAM have to hold besides model weights?
A downloaded model file is only the first item in the memory budget. During inference, the GPU also stores the KV cache—the short-term memory for the current conversation—plus intermediate activations, runtime buffers and the graphics desktop. Image and video pipelines add their own encoders, decoders, latent tensors and frame data.
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The Vulkan Documentation Project describes the model-size figure on a download page as incomplete because KV-cache usage grows with context length. Its worked example reserves about 6GB on a 24GB RTX 4090 for the operating system and Vulkan resources. An 18GB 30B Q4 model then fills the remaining capacity before the cache grows, leaving no safety margin. When combined requirements exceed physical VRAM, the operating system can move weights or Vulkan resources into system RAM; that avoids an immediate crash but is dramatically slower.
- Weights: the parameters stored by the model, affected by parameter count and precision.
- KV cache: memory that expands as an LLM conversation becomes longer; a 16k context can add roughly 1–2GB in the Vulkan guide’s example.
- Activations and workspaces: temporary tensors needed while generating each token, image or frame.
- System and application overhead: the desktop, driver allocations and other GPU programs competing for the same pool.
For that reason, treat a card’s advertised capacity as a ceiling, not as memory available entirely to the model. A model that mathematically equals your card’s capacity may fail as soon as a conversation, image resolution or background application increases the working set.
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How much VRAM do local LLMs need?
Small quantized models can run in 8GB, 12–16GB covers many 7B–14B use cases, and 24GB makes 30B-class Q4 models practical when context and background usage are controlled. Larger parameter counts, higher precision and long contexts quickly push the requirement into 32GB-plus territory.
Representative LLM memory figures
| Model or condition | Approximate memory | What the figure means |
|---|---|---|
| 7B–8B quantized model | Often workable in 8GB | Keep context and other GPU use modest; CPU offload may still be required. |
| 12B model | About 8GB for the model in the Vulkan guide | Leaves less room for cache, desktop overhead and runtime buffers than the file-size number suggests. |
| 30B Q4_K_M model | About 18GB | Fits the weights on a 24GB card, but not with unlimited context or competing applications. |
| 16k LLM context | Roughly 1–2GB of additional KV cache | The increase depends on architecture and settings; it is extra memory beyond the weights. |
Quantization is the main reason the same model can fit in different tiers. A Q4_K_M build stores weights at roughly four-bit precision, making the cited 30B model about 18GB instead of the much larger footprint of an unquantized version. NVIDIA’s llama.cpp guidance maps 8–16GB cards to smaller Qwen models, 16–24GB to a larger class and 24–48GB to another tier. These are starting bands rather than guarantees: context length, backend and offload behavior decide whether a particular file loads.
What a 24GB card can and cannot do
A 24GB GPU is well suited to a 30B-class Q4 model, but it is not a promise that every 30B model will run at every context length. In the Vulkan example, reserving 6GB for the system leaves exactly enough for an 18GB 30B Q4 model and nothing for cache growth. Reducing the context to around 4,096 tokens or selecting a smaller model is the practical response when allocation errors appear.
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Higher precision raises the requirement again. The same parameter count in FP16 can need roughly twice the weight storage of an equivalent eight-bit representation and far more than a Q4 build. Always check the exact quantization and file format you intend to load rather than relying on the model’s parameter count alone.
How much VRAM do image generators need?
For still images, 4GB is the published minimum for Stable Diffusion 1.5 at 512×512, while 8GB is the published minimum for SDXL at 1024×1024. Those minimums describe a basic generation setup, not unlimited batch size, high-resolution refinement or multiple control components.
Baseline and newer image pipelines
- SD1.5: InvokeAI lists 4GB as the minimum at 512×512. Lower memory modes or offload may be necessary on cards near that floor.
- SDXL: InvokeAI lists 8GB as the minimum at 1024×1024. Extra extensions, larger batches or an upscaling pass consume additional memory.
- Z-Image-Turbo: NVIDIA’s 2026 quick start calls for about 24GB of GPU memory. Its stated components include a 12GB diffusion model and an 8GB text encoder before activations, leaving only a small remainder for the rest of the pipeline.
- Stable Diffusion 3.5 Large: NVIDIA reports more than 18GB in its base form. FP8 reduces that figure to about 11GB, a reduction of approximately 40 percent.
Resolution, batch size, precision and the number of simultaneously loaded components matter as much as the checkpoint name. A 12–16GB card is a balanced choice for many SDXL and moderate multi-component workflows; a 24GB card gives substantially more room for newer pipelines and high-resolution processing.
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How much VRAM does local AI video generation require?
Video generation needs far more memory than a single still because the pipeline retains information across many frames and often loads multiple large models. NVIDIA’s ComfyUI documentation gives three representative peaks:
| Workflow tier | Representative setup | Peak GPU memory |
|---|---|---|
| Starter | 720p Wan workflow | Approximately 80GB |
| Intermediate | Two-model graph | Approximately 100GB |
| Advanced | Hunyuan workflow at 1080p | Approximately 120GB |
These are peak values, not universal requirements. NVIDIA explicitly notes that peak VRAM depends on resolution, frame count and precision. Reducing any of those can lower the working set, but a consumer card with 12–24GB remains far below the documented peaks for the larger graphs. When a workflow still exceeds local capacity after lowering settings, a hosted GPU becomes a more realistic option than forcing extensive system-RAM offload.
Which VRAM tier matches your workload?
| Installed VRAM | Typical local use | Main caveats |
|---|---|---|
| 4–8GB | SD1.5 at 512×512; 7B–8B quantized LLMs | Keep context and resolution modest; offload may be needed. |
| 12–16GB | SDXL at 1024×1024; 7B–14B LLMs; some larger models with offload | Reserve memory for the desktop, KV cache and runtime buffers. |
| 24GB | 30B-class Q4 LLMs; demanding image workflows; multiple components | A 16k context can add 1–2GB, and concurrent applications can erase the remaining headroom. |
| 32GB or more | Larger local models, longer contexts and heavier image pipelines | Price and availability rise; workload settings still determine fit. |
| 80–120GB | Advanced local video workflows in NVIDIA’s documented tiers | Resolution, frame count and precision change the peak. |
Choose 8GB only when your target is entry-level image generation, small quantized models or experimentation with offload. Choose 12–16GB for a general-purpose local-AI machine. Move to 24GB when 30B Q4 models, newer image pipelines or meaningful context headroom matter. Consider 32GB-plus when you know you need larger models, long contexts or several stages loaded at once.
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How can you calculate whether a model will fit before installing it?
- Identify the exact artifact. Record parameter count, quantization such as Q4_K_M, precision such as FP8 or FP16, and whether the application loads separate text encoders or adapters.
- Start with the weight size. Use the published file-size estimate as a floor, not a complete requirement. The Vulkan guide’s 30B Q4_K_M example is about 18GB; its 12B example is about 8GB.
- Add dynamic memory. Budget KV cache for the intended context, plus activations and temporary workspaces. A 16k context can add roughly 1–2GB in the cited example.
- Subtract your real overhead. Leave room for the operating system, desktop, driver and other GPU applications. The 24GB worked example reserves 6GB before loading the model.
- Check application features. Confirm whether the software supports CPU or disk offload, reduced precision and adjustable context, resolution or frame count.
- Test the full workload. A model may load at idle and fail during a long prompt, a second image component or a multi-frame render. Watch actual VRAM use while performing the operation you care about.
What should you do when a model does not fit?
Change the largest memory drivers first, then retest after each adjustment:
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- Lower the LLM context length; moving toward about 4,096 tokens is the Vulkan guide’s practical response to pressure on a 24GB card.
- Reduce image resolution, batch size or video frame count.
- Use a lower-memory precision or quantized file, such as FP8 where the application supports it.
- Select a smaller model or a simpler graph with fewer simultaneously loaded components.
- Enable CPU or disk offload if supported, understanding that system RAM and storage are much slower than VRAM.
- Close unrelated GPU applications and restart the workload to clear allocations.
- If the reduced workload is still too large, use a GPU with more physical VRAM or move the job to hosted capacity.
How should you compare two GPUs for local AI?
Capacity is the first filter, but it is not the only specification that affects the experience. Compare these factors for the workload you actually plan to run:
- Usable VRAM: estimate capacity after desktop and operating-system overhead, not the number on the box.
- Precision and quantization support: verify that your backend can run the formats you intend to use.
- Context and output size: longer conversations, higher image resolution and more video frames increase peak allocation.
- Offload support: CPU or disk offload can make an otherwise impossible model load, but reduces speed.
- Generation throughput: memory bandwidth, backend implementation and thermals affect tokens per second or images per minute after the model fits.
- Upgrade cost and availability: NVIDIA’s current local-AI hardware table lists GeForce systems up to 32GB, while 80–120GB configurations are generally a different class of hardware.
What is the practical buying decision?
Match the card to the largest workload you expect to run regularly, then leave measurable headroom for context, resolution and background use. Buying exactly enough for today’s model can force a downgrade when a longer prompt, a newer checkpoint or a second pipeline is added. Conversely, paying for extreme capacity makes little sense if you only run SD1.5 or small quantized LLMs.
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Before purchase, verify the current model files, quantization options, software backend, prices and retailer stock. Model releases and GPU availability change, while the underlying rule remains stable: reserve memory for everything that runs alongside the weights.
Frequently Asked Questions
Can system RAM replace VRAM for local LLMs?
Applications that support CPU or disk offload can place part of a model outside the GPU, but transfers across the system bus add substantial latency. Offload is useful for testing or occasional prompts; it is not equivalent to having enough VRAM for sustained generation.
Why does the same model fit in one program but fail in another?
Backends use different allocators, cache layouts and offload strategies, and some pipelines load encoders or adapters that others keep on the CPU. Compare the application’s peak allocation and settings rather than assuming a model’s download size guarantees identical behavior everywhere.
Does more VRAM automatically make generation faster?
More capacity mainly lets you load larger models or use longer and higher-resolution jobs. Once a workload fits, speed depends on memory bandwidth, compute resources, backend support and thermals, so a card with less capacity can be faster for a small model.
How much headroom should I leave when planning a GPU?
There is no universal percentage because cache size and pipeline structure vary. Leave enough unallocated memory for your longest intended context, the desktop and any second component you routinely keep open; if allocation is already close to the limit at idle, the configuration is fragile.
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