TurboQuant Simulator
The TurboQuant Simulator lets you compare KV-cache memory before you commit to a model setup. Enter layers, heads, sequence length and batch size, then see FP16 against 4-bit, 3-bit and 2-bit in seconds, with the longest context your budget allows.
TurboQuant is a KV-cache compression method from Google Research (based on PolarQuant and a 1-bit Quantized Johnson-Lindenstrauss step) that shrinks the memory an AI model uses to remember a conversation. This simulator applies the standard KV-cache size formula — 2 (keys + values) × layers × KV heads × head dimension × sequence length × batch — at 16-bit versus TurboQuant bit widths. It is an educational estimate tool, not an official Google product: real results vary by model and implementation, and small quantisation overheads are ignored.
How to use this TurboQuant calculator
- Enter your model details: layers, KV heads and head dimension.
- Set the sequence length and batch size you plan to run.
- Compare FP16 with 4-bit, 3-bit and 2-bit memory side by side.
- Check the maximum context that fits your memory budget, then adjust the inputs.
What it helps you plan
As a KV cache memory calculator, it applies the standard keys-plus-values formula to your model, then scales the result by precision, so you can see how PolarQuant and its error-correction step shrink the cache. That makes it a practical quantization memory simulator for planning LLM inference memory, especially when context length memory is the bottleneck rather than weights. Try it as an LLM memory calculator before you raise batch size or sequence length, and use the KV cache calculator view to check whether a lower precision is worth the quality trade-off.
- FP16 versus 4-bit, 3-bit and 2-bit comparison
- Maximum context for a given memory budget
- Close estimates, with small overheads ignored
Related tools
Frequently asked questions
In the paper’s tests, 4-bit was near-lossless and 3-bit had small trade-offs. 2-bit is aggressive and experimental — this simulator includes it so you can see the memory effect, but expect a real quality risk at that level.
With the standard formula: 2 (keys + values) × layers × KV heads × head dimension × sequence length × batch size, multiplied by bytes per value — 2 bytes at FP16, or bits ÷ 8 at TurboQuant precision. Small real-world overheads are ignored, so treat results as close estimates.
In pure bit terms, 4-bit values are 4× smaller than 16-bit; the research reports KV memory reductions of 6× or more in its test setups with no measured accuracy loss, because the method also reduces the bits needed per value overall. This simulator shows the simple bit-width comparison for your own numbers.
Yes, free on iWebSeoTool, with no sign-up.
No. It is an independent educational simulator by iWebSeoTool, based on the published TurboQuant approach. It is not affiliated with Google.
When a model generates text, it stores key and value vectors for every token it has processed, in every layer, so it does not recompute them. That memory — the KV cache — grows with longer conversations and more users, and is often the real limit on context length.
TurboQuant is a compression method for the KV cache of large language models, published by Google Research. It combines PolarQuant (which converts vectors to polar coordinates) with a 1-bit error-correction step (Quantized Johnson-Lindenstrauss), shrinking inference memory while aiming to keep output quality.