Hi everyone, I've been reading up on Google's recent TurboQuant announcement from a few days ago (compressing the KV cache down to 3-4 bits with supposedly zero accuracy loss), and I'm trying to wrap my head around the practical implications for our daily setups.
We already have great weight quantization formats like GGUF...but since TurboQuant specifically targets the KV cache rather than the model weights, I have a few questions for those who have dug into the paper or tried the early mlx / llama.cpp forks:
General Local Processing Throughput vs. Memory: Is the primary benefit here just about surviving massive context windows (like 16K–32K+ tokens) without OOMing, or does the reduced memory bandwidth actually translate to massive generation speedups (tk/s) for standard prompt sizes too?
Consumer Hardware: Google claims up to an 8x speedup on H100s. How well does this 2-stage rotation math actually scale on consumer Nvidia GPUs or Mac Apple Silicon? Are we going to see that same IO bottleneck relief?
The Mobile & Edge Factor (My biggest question)
RAM Constraints: For phones and edge devices, unified RAM is our biggest enemy. If the KV cache is now ~5x smaller, does this mean running 7B/8B models with decent context sizes on a standard 8GB/12GB smartphone is finally practical without the OS aggressively killing the app?
Battery and Compute Overhead: TurboQuant is supposed to be "accelerator-friendly" and data-oblivious, but does the mathematical overhead (the random rotations and dequantization) hit mobile NPUs/CPUs hard? I'm wondering if the reduced memory I/O saves enough power to offset the extra compute, or if it'll drain a phone battery in 10 minutes.
If anyone has run early benchmarks, or just has educated guesses on how this shifts the landscape for mobile LLMs, I'd love to hear your insights. Thanks!
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