Calculate GPU VRAM requirements for LLM Key-Value (KV) cache across context lengths, batch sizes, and Grouped Query Attention (GQA).
Execution runs 100% locally inside the browser sandbox using HTML5 Canvas, Web Cryptography Subtle API, and Web Workers. Zero egress.
Zero network latency. Operates completely offline with zero dependencies on third-party backend servers or cloud services.
Built according to official RFC specifications, cryptographic test vectors, and enterprise-grade data transformation standards.
Output = Process(Input_Buffer, Options)Input_BufferOptionsOutput“Running a standard operation in LLM KV Cache Memory Footprint & Attention (MHA / GQA / MQA) Sizing Calculator.”
Verify that input data matches supported format criteria.
Transforms the input data according to configured options.
The operation succeeded with zero server round-trips.
Enter your parameters or upload files into LLM KV Cache Memory Footprint & Attention (MHA / GQA / MQA) Sizing Calculator.
The tool executes automatically in real time.
Copy your results to clipboard or download the output file.
LLM KV Cache Memory Footprint & Attention (MHA / GQA / MQA) Sizing Calculator runs entirely inside your web browser sandbox using modern client-side APIs. No user data, files, or inputs are sent to remote servers.
Yes. The interface is fully responsive and touch-optimized for mobile phones, tablets, and desktop computers.
Utilifie is 100% free with zero data tracking. Support continuous development and tool elevations.
Zero-egress companion tools in the AI Utilities suite
Calculate Token-per-Minute (TPM), Request-per-Minute (RPM), and token refill burst parameters for AI rate limiters.
Calculate Key-Value cache VRAM memory footprints ($Memory = 2 imes ext{Layers} imes ext{Heads} imes ext{Dim} imes ext{SeqLen} imes ext{Batch} imes ext{Bytes}$) across quantization precisions.
Calculate Shannon entropy, Gini coefficient, and representation ratios for multi-class AI training datasets.
Calculate API inference cost given prompt and completion token counts.