modgate

There was a good data point on r/LLMDevs this week: two weeks of head-to-head quant testing on a 5080 showed that above Q4 most quants are statistically indistinguishable. That matches my experience — the cliff is below Q4, and it's not uniform across architectures. With MoE models the hidden cost of aggressive quants is routing drift: the router's logits get noisier, experts get picked wrong, and you lose quality in ways that don't show up on standard perplexity evals. MTP draft models are even more sensitive — a low-quant draft quietly caps your speculative-decoding acceptance rate while the headline tok/s still looks fine. Have you compared router decision distributions between Q4 and Q2, or just end-task accuracy?

Routing makes even more sense for voice than text, but the constraint space is trickier: latency budget (round-trip vs streaming), WER on accented speech, and TTS naturalness all trade off non-linearly. In our voice pipeline, DeepSeek-V4-Pro plus a small dedicated STT beat an end-to-end frontier voice model on cost-per-minute by ~10x while staying inside a 300ms added-latency budget - purely because we could mix and match components. The hard part is honest benchmarking: WER numbers are only comparable within the same eval set, so a "find the optimal combo" service lives or dies by its methodology. Do you expose per-component benchmarks (STT WER, TTS MOS) separately, or only the combined scores?