FareedKhan-dev/kimi-k3-in-c

A 2.78-trillion-parameter model. One CPU. 8 GB of RAM.

Kimi K3 inference in portable C99. No BLAS. No framework. No GPU.

The same 2.78-trillion-parameter model, the same answer, on whatever machine you own. More memory only buys speed:

Same short prompt at every size, and the output is byte-identical from the smallest machine to the largest; only the clock changes. One machine, 124 cores, fast NVMe drive: the first three rows still read the model from disk each step, so a slower drive is slower there, while the 128 GB+ row keeps everything in memory and no longer waits on the disk. On that same machine v1.0.0 made the math per token about 8× lighter, a follow-up question in a chat 3.9× faster, and long prompts about half as costly. (A token is roughly a short word-piece; the two runnable demos below are the original captures on a slower drive, so their clock reads a little higher.) Full data in docs/data/.

I am open to AI research roles and PhD positions. CV.

The macOS, Windows and NEON ports, chat mode, the ultra preset, the faster MXFP4 kernels and a long list of parser fixes came from the people below. Who did what.