Ideas Weave Every Narrative with AI.
Unsloth compressed Kimi K3 from 1.56TB to 594GB using 1-bit quantization — and it kept 78.9% of its accuracy. This isn’t just a compression trick. It’s a signal that the industry’s obsession with precision is built on shaky assumptions, and the future of AI deployment might be radically smaller than anyone expected.
Self-hosting LLMs can yield 34x first-year ROI—but only if you maintain 90% GPU utilization. The AI industry is regressing to old-school IT asset management, and the bottleneck is no longer model intelligence. It’s logistics. Renting is convenient; owning is a religion.
When you run LLMs on consumer RTX GPUs, vLLM and SGLang silently fall back to FlashAttention-2 — a kernel from 2022. Not because your hardware can’t handle FA-3/4, but because nobody bothered to port them. A first-principles rebuild of attention kernels proves the core techniques are architecture-agnostic, meaning you’re leaving real performance on the table every single inference call.