FYVIE AI

ONE BIT LLMs

Coming Soon

1B Watches 7B Phones 12B Laptops 24B Desktops 32B Desktops

Powerful on-device AI

Fyvie AI runs offline on your phone, laptop, desktop, spark, or any other personal device. No data gets set to us, no code, no conversations. Everything runs on your device, offline, in total privacy.

Memory Footprint

Models that needed a server rack now sit in the SRAM of a phone. No quant-aware retraining loop. No mixed-precision inference path.

16× smaller
Versus FP16 baseline

Energy Footprint

Most of an LLM's compute is matrix multiply. At 1-bit, multiplies become XNOR + popcount — gates that cost a fraction of a femtojoule.

70× lower power
Theoretical, vs FP16 GEMM

Unlimited Tokens

The model runs locally. No GPU clusters. No per-token API meter. No rate limits. Use AI as much as you want, with no hidden costs.

UNLIMITED tokens
No API limits

Existing Hardware

Designed to run on the silicon shipping today — ARM, Qualcomm, Apple Silicon — while future-proofing for custom 1-bit accelerators.

GPUs & NPUs

State of the art*
And only getting better

Perplexity is the standard measure of language-model quality - lower is better. In an internal feasibility study in the context of an underparametised model, our model beats BitNet by 6× and 2-bit QAT by 18×, while using less memory than either. Current methods collapse on the task, our method retains a level of performance.
*In internal testing.

6×
Higher performance than BitNetAt 1 bit vs their 1.58 bits — and still ahead on perplexity.
50%
Less memory than BitNetBitNet uses 1.58 bits per weight; Fyvie uses exactly 1.
SOTA
1-bit classAcross all tested quantisation methods in internal testing.
82M Param LLM · Perplexity
↓ Lower is better
Source: Internal feasibility study, high perplexity expected, 2026

Powering the next
on-device revolution