K2 Horizon's frozen-model LoRA gives 3x faster inference, trained on 20T tokens
rohanpaul_ai · x · 2026-09-11
K2 Horizon ships with Uno Diffusion, a LoRA adapter that leaves the original autoregressive model frozen while learning to generate blocks of tokens in parallel — no separate draft model, no base-model migration, and IFM reports roughly 3x faster inference with no quality loss.
Other details:
- Each model pretrained on 20T tokens mixing web, code, math, science, multilingual and synthetic data
- 17% of the pre-training corpus contains explicit reasoning trajectories; 10T synthetic tokens used
- Post-training involved 100M+ unique generated tasks
- The whole fleet shares one training, chat, tool-calling and deployment stack, with day-zero vLLM, SGLang and Ollama support across NVIDIA, AMD and Cerebras hardware
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