Our 1-bit quant of Hy3 295B runs 2.2x faster than the cloud API with no quality loss

We quantized Tencent's Hy3 295B down to 1 bit and got a 92GB IQ1_M GGUF, small enough for one 4-GPU box. We ran it on 4x RTX 5090 against the same Hy3 over the cloud API. Both got the same one-shot task. Each model built a self-playing retro game in one HTML file. We ran three rounds with Flappy Bird, Arkanoid and Snake, reasoning on high.

Hy3 is a 295B MoE with 21B active (192 experts, top-8, 80 layers) plus a 3.8B MTP layer. We quantized straight from the original weights with our own importance matrix over calibration_datav3. The experts drop to 1-bit class, attention, embeddings and the output head keep q4-q8, which lands at ~2.5 bits/weight. The MTP layer stays in, so llama.cpp runs it with self-speculative decoding. Memory stays friendly: ~114GB at 64K context. The hy_v3 arch landed in llama.cpp master, so a fresh build runs it out of the box.

Outputs:
Hy3 1-bit local: 76.9K tokens · 15.5 min · ~83 tok/s with reasoning
Hy3 cloud API: 75.1K tokens · 34.3 min · ~37 tok/s

The games came out at the same level. Birds thread the pipes, bricks break, the snake eats and grows, nothing froze or crashed. Both versions made the same slip: the snake can cross itself and the game never ends. The speed gap comes from MTP drafting plus zero queue.

tested on llama.cpp, the same engine that powers our local AI app: Atomic.Chat (I'm from the Atomic team, happy to answer questions)