Ideas Weave Every Narrative with AI.
Inkling-Small is called “small” but needs 128GB of unified memory. The paradox reveals an overlooked truth: the real metric for local AI deployment isn’t total parameters — it’s the active-to-total ratio. High sparsity enables brutal quantization without quality loss. Most benchmarks ignore this entirely, and it’s costing engineers real money in wrong hardware decisions.
A 35B model running at 225 tok/s on a 4090 sounds like a breakthrough — until you realize the 2-bit quantization may be quietly destroying the model’s reasoning ability. The missing accuracy graph is a red flag: speed without fidelity is a dangerous trade-off for anyone who needs reliable, long-chain thinking. Don’t confuse throughput with intelligence.
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.