MLX Studio eyes TurboQuant support

Hot take: this is the kind of unsexy plumbing that can change the local AI experience more than another flashy benchmark. If TurboQuant survives real-world integration overhead, it turns “longer context on smaller hardware” into a product capability, not just a paper claim.

• –TurboQuant’s paper claims near-optimal distortion and says KV-cache quality stays neutral at 3.5 bits/channel, with only marginal degradation at 2.5 bits/channel. [paper](https://arxiv.org/abs/2504.19874)
• –MLX Studio already advertises native KV-cache quantization in vMLX Engine, so TurboQuant looks like a natural next step for its local-inference stack. [app](https://mlx.studio/)
• –The real risk is runtime overhead: random rotation, residual coding, and any extra kernel work have to stay cheap enough to matter in a real Mac app.
• –If the implementation is shared publicly, the [LocalLLaMA thread](https://www.reddit.com/r/LocalLLaMA/comments/1s350sj/implementing_turboquant_to_mlx_studio/) could become a useful reference point for people chasing longer contexts on smaller machines.