VRAM is a fit constraint, not a quality score

A GPU with more VRAM can hold larger model weights and more runtime state, but more memory does not automatically make every model faster or better. GPU architecture, memory bandwidth, runtime support and the actual workload still matter.

Why model parameter count is not enough

A model described as 7B, 14B or 32B gives you its approximate parameter scale, not the exact memory footprint you will see. Quantisation stores weights with fewer bits per parameter, reducing memory use at the cost of some precision. Runtime overhead, context/KV cache and other allocations add memory beyond the model file itself.

That means a model that appears to “fit on paper” can still exceed VRAM when you increase context length or use a runtime with additional overhead.

Useful conservative bands

For a first-pass estimate, think in broad bands rather than guarantees:

  • 0–4 GB VRAM: small models and CPU/RAM-heavy workflows; useful for experimentation but limited GPU headroom.
  • 6–8 GB: a practical entry range for many smaller quantised local models.
  • 10–12 GB: more flexibility for medium-size quantised models and longer contexts.
  • 16 GB: strong local-AI flexibility for a wider range of models and workflows.
  • 24 GB+: substantially more room for larger models, higher precision or heavier context use.

These are deliberately broad heuristics, not promises that a specific model will run well. Model format and runtime compatibility must still be checked.

What if the model does not fit entirely in VRAM?

Many local runtimes can place part of the workload in system RAM and use the CPU for some layers. This can make a larger model technically runnable, but speed may fall sharply compared with full or mostly-GPU execution. “It loads” and “it feels good in an interactive workflow” are different success criteria.

System RAM therefore matters too. If you plan to run larger models with CPU offload, leave memory for Windows, the runtime, context and other applications instead of sizing RAM only around the model file.

Context can change the answer

Long conversations and large document contexts consume additional memory. A setup that works at a modest context window may fail or slow down when the context grows. For coding agents and document-heavy workflows, context requirements deserve the same attention as raw model size.

Use workload-first selection

Choose the smallest model that performs the required task reliably before chasing the largest model your hardware can load. Coding, summarisation, chat, vision and agent planning may benefit from different models. LLMRadar is designed around this hardware-and-goal view, while HardwareRadar focuses on the machine itself.

For a fast first pass, try the Local AI Readiness Estimate. Then use Which Local LLM Can My PC Run? to turn RAM, VRAM and workload into a more careful shortlist.

Bottom line

VRAM is one of the clearest local-AI constraints, but it should be treated as a range. Quantisation, context, runtime support and CPU/RAM offload determine whether a model merely loads or becomes practical.