Free planning tool
VRAM Calculator for Local AI Models
Estimate how much GPU VRAM you may need for local LLMs, quantized models, Stable Diffusion, and AI workloads based on model size, quantization, and context length.
Estimate GPU VRAM for LLMs and AI workloads
A VRAM calculator for LLM planning is useful when you are deciding whether a local AI workflow is realistic on a single GPU. Model size alone is not enough to answer that question. A local language model loaded in FP16 can need substantially more memory than a quantized version of the same model, while longer prompts and conversations add memory pressure through the context window and KV cache. This estimator gives you a starting range before you test a specific model, runtime, and GPU configuration.
Choose a model size, a rough quantization level, and the context preset that best represents your intended workload. The output is meant for early GPU memory planning: exploring local LLM GPU memory, evaluating whether a build should target a larger VRAM tier, or deciding where further benchmarking is necessary. The same caution applies when researching Stable Diffusion VRAM needs and other AI image workloads, where resolution, batch size, model pipeline, and extensions can materially alter usage.
The estimate is deliberately conservative and transparent. It uses simple memory assumptions for FP16, INT8, and INT4 weights, adds a context allowance, then applies your selected safety margin. It does not report tokens per second, generation speed, or official hardware support. Use it to narrow your initial options, then verify the selected runtime, quantization format, driver stack, and actual model on the hardware you plan to run.
Visual workflow
Turn workload assumptions into a planning tier
Each input changes the rough estimate. The output is a direction for further validation, not a verified performance claim.
- Model sizeParameter scale
- QuantizationWeight format
- ContextMemory overhead
- VRAM estimateRough output
- GPU tierResearch next
Rough estimate
7.2 GB
Recommended planning minimum: 8 GB VRAM
- Planning tier
- 8 GB VRAM planning tier
- Selected mode
- INT4 / medium context
- Confidence
- Low - estimate requires validation
7.2 GB rough estimate; plan for at least 8 GB VRAM before runtime-specific validation.
This is a rough estimate, not an official benchmark. Actual VRAM usage depends on runtime, quantization format, context length, KV cache, batch size, drivers, and model architecture.
- The estimate uses a simple parameter-memory multiplier plus a context overhead allowance.
- Quantization format and runtime implementation may change real memory use.
- Validate a chosen model and runtime on target hardware before purchasing a GPU.
This calculator provides a rough estimate only. Actual VRAM usage depends on runtime, quantization format, context length, KV cache, batch size, drivers, and model architecture.
How this VRAM estimate works
The MVP estimate applies approximately 2 GB per billion parameters for FP16, 1 GB for INT8, or 0.5 GB for INT4, plus a short, medium, or long context overhead and a configurable safety margin. These assumptions are a planning heuristic only and require validation against the target runtime.
Recommended GPU VRAM tiers
Results are grouped into planning tiers such as 8 GB, 12 GB, 16 GB, or 24 GB and above. A tier is not a GPU endorsement. GPU records and model requirements remain draft until sourced specifications and controlled workload tests are available.
Frequently asked questions
How much VRAM do I need for a 7B model?
It depends on quantization, context length, runtime, and overhead. A quantized 7B configuration may require far less memory than FP16, so use the calculator as an initial estimate and validate the intended runtime.
Is 8GB VRAM enough for local AI?
It may be sufficient for some smaller or quantized workloads, but it is not a universal threshold. Image generation, longer contexts, larger batches, and different runtimes can increase memory demand.
How much VRAM do I need for a 70B model?
Large models can require substantial memory even after quantization. Use the estimate to identify a planning tier, then confirm the chosen format and runtime with a documented test before choosing hardware.
Does quantization reduce VRAM usage?
Quantization generally reduces weight memory compared with higher precision formats, but actual VRAM use also includes context, KV cache, runtime overhead, and other implementation details.