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Nari Labs walked through five coordinated optimizations that bring Qwen3-TTS 1.7B to sub-50 ms p95 time-to-first-audio on a single H100, at $2 per million characters — against ElevenLabs at $100/M. None of the five changes require a new model architecture. Each targets a specific latency source, and the gains compound.
Read more →Two independent projects landed on Hacker News today pursuing the same idea from different directions: fit something genuinely useful into the smallest possible package. One runs a 28.9M-parameter LLM at 9.5 tok/s on an $8 ESP32-S3 by keeping the embedding table in flash. The other fits complete voice synthesis into 9.36M parameters that run faster than real-time on a laptop CPU. Neither is frontier work. Both are instructive about what constrained-environment AI actually requires.
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