🎮 LLM GPU VRAM Calculator
Estimate LLM GPU VRAM for weights, KV cache, activations, optimizer states, gradients, batch size, sequence length, and safety overhead.
Calculate LLM GPU VRAM
What this LLM GPU VRAM calculator calculates
Estimate whether a language model fits in GPU memory for inference or training. The calculation separates weights, KV cache, activations, gradients, optimizer states, and runtime overhead.
Treat the result as a planning estimate. Actual frameworks use memory-efficient attention, checkpointing, sharding, offload, and allocator behavior differently.
LLM GPU VRAM Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. AI models, tokenizers, runtimes, accelerators, cloud services, and provider billing rules differ. Validate important decisions with measured data from the exact model, hardware, framework, and pricing plan you intend to use.
Worked example
A 7B FP16 model needs roughly 14 GB for weights alone. Long context, larger batch size, and training states can multiply the requirement.
How to use the result
Treat the result as a planning estimate. Actual frameworks use memory-efficient attention, checkpointing, sharding, offload, and allocator behavior differently.
- Start with representative production assumptions rather than best-case demos.
- Run a low, expected, and high scenario to understand the range.
- Record model version, pricing date, hardware, precision, and workload details.
- Replace assumptions with observed p50 and p95 measurements after testing.
Common mistakes to avoid
- Calculating only weight memory and ignoring KV cache.
- Using total system GPU memory without leaving allocator and runtime headroom.
- Applying a generic training multiplier without checking optimizer, checkpointing, and sharding strategy.
Frequently asked questions
What is KV-cache memory?
It stores attention keys and values for processed tokens so autoregressive generation does not recompute the entire prefix.
Why does sequence length affect VRAM?
KV cache and many activations grow with sequence length; attention computation may grow even faster.
Can multiple GPUs combine VRAM?
Model and tensor parallelism can distribute memory, but not every buffer splits evenly and communication overhead increases.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This formula describes how token load, memory and throughput constrain an online model deployment. Assumption: Tokenizer, model architecture, batching, queueing, runtime, hardware and traffic distribution must match the intended production system.