LLM VRAM Calculator
Estimate GPU memory requirements for local LLM inference. Adjust model size, quantization, and context length to see how much VRAM you will likely need.
Weights = params × precision bytes
KV cache = 2 × layers × hidden size × context × batch × kv bytes
Total = weights + KV cache + runtime overhead + framework reserve
Note: This is an estimate for inference. Training, LoRA fine-tuning, and speculative decoding often require additional memory.
Why a VRAM calculator for LLMs matters
If you run large language models locally, VRAM is the bottleneck you feel first. CPU, SSD speed, and RAM all matter, but when your model does not fit in GPU memory, inference slows down dramatically or fails to start. A practical LLM VRAM calculator helps you estimate memory before you download 40 GB of model files or buy the wrong graphics card.
People usually ask questions like:
- Can I run a 7B model on an 8 GB GPU?
- Will a 24 GB card handle long context windows?
- How much memory do I save with 4-bit quantization?
- When do I need multi-GPU sharding?
This page gives you a calculator and the reasoning behind it, so you can size your setup for chatbots, coding assistants, RAG pipelines, and local AI experimentation.
What consumes VRAM in an LLM?
1) Model weights
Weights are the core parameters of the model. Their memory footprint scales with parameter count and precision. A larger model or higher precision always means higher VRAM usage. For example, moving from 4-bit to 16-bit can roughly quadruple weight memory.
2) KV cache
During generation, transformer models store key/value tensors for each token in context. The longer your prompt and output, the larger this cache grows. This is why a model that fits at 4K context can become unstable or out-of-memory at 32K context, even if weights are unchanged.
3) Runtime overhead
Inference engines allocate buffers, temporary tensors, CUDA kernels, and metadata. Different stacks (vLLM, TensorRT-LLM, llama.cpp, exllama, etc.) have different overhead profiles. You should always add a safety margin.
How to use this calculator
- Select a preset or enter custom model values.
- Pick quantization for weights (4-bit, 8-bit, 16-bit).
- Set context length and batch size realistically for your workload.
- Choose KV cache precision and overhead percentage.
- Calculate and compare against your actual GPU VRAM.
For conservative planning, leave extra room (10% to 25%) beyond the estimate. Real workloads, adapters, and tool usage can spike memory unexpectedly.
VRAM planning examples
Example A: 7B model, 4-bit, 4K context
This is often a sweet spot for consumer GPUs. With Q4 weights and moderate context, many setups fit within 8 to 12 GB, depending on runtime overhead and implementation.
Example B: 13B model, 8-bit, 8K context
Memory climbs quickly. You may need 16 to 24 GB to keep performance smooth. If you increase batch size for throughput, plan for more.
Example C: 70B class model, quantized, long context
Even quantized 70B models can push beyond a single consumer GPU. Multi-GPU inference, aggressive quantization, or partial CPU offload becomes necessary.
Ways to reduce VRAM requirements
Use stronger quantization
Moving from FP16 to 8-bit or 4-bit usually provides the largest memory win. The trade-off is potential quality or stability changes, depending on model and quantizer.
Lower context length
KV cache grows linearly with tokens. If your app does not need giant prompts, reducing context has immediate memory impact.
Reduce batch size
Higher batch improves throughput but multiplies cache and activation needs. For low-latency chat, batch size 1 is common.
Use optimized inference engines
Kernel fusion, paged attention, and memory-aware allocators can reduce overhead. Different backends can vary by several GB on identical models.
Offload strategically
CPU offload can make oversized models runnable, but latency rises. This is useful for experimentation, less ideal for production chat responsiveness.
Quick FAQ
Is this exact?
No. It is a practical estimator. Real memory depends on architecture details, tokenizer behavior, software stack, and generation settings.
Does this calculator cover training?
Not fully. Training and fine-tuning need gradients, optimizer states, and larger activation footprints. That can require multiple times more VRAM than inference.
Why does context length hurt so much?
Because KV cache accumulates per token across all layers. Long prompts and long outputs both contribute.
Bottom line
A good VRAM calculator for LLMs keeps your local AI setup predictable. Estimate first, then choose quantization, context, and hardware confidently. Use this tool as your baseline, then validate with your exact runtime stack and target workload.