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A 2.78 trillion-parameter Kimi K3 mixture-of-experts model was run on a single…

2026-08-04 · 12:41 UTC ·@DataChaz ·1 min read

Brief

Kimi-k3-in-c runs the 2.78T-parameter Kimi K3 MoE model on a single CPU with 8 GB RAM by activating only 16/896 experts per token, streaming the dense trunk layer-by-layer from NVMe, and using 4-bit compressed weights. The 176 KB C99, zero-dependency implementation is fully open source, requires ~1.7 TB disk, and yields byte-identical outputs across memory configurations but is slow (~32 s/token at 8 GB).

Why it matters

A 2.78 trillion-parameter Kimi K3 mixture-of-experts model was run on a single CPU with just 8 GB RAM using the 176 KB C99 project 'kimi-k3-in-c' (100% open source); only 16 of 896 experts are activated per token so 93% of the model is kept on NVMe and loaded on demand.

Key details

  • Weights are stored and multiplied in compressed 4-bit form and the dense trunk is streamed one layer at a time, producing byte-identical outputs whether run with 8 GB or 224 GB RAM; the run needs ~1.7 TB free disk and is ~32 seconds per token at 8 GB.
Source evidence

SOMEONE JUST RAN A 2.78 TRILLION PARAMETER MODEL ON A SINGLE CPU WITH JUST 8 GB OF RAM

No GPU. No framework. Just six files of portable C99.

And he made it 100% Open Source.

Kimi K3 normally comes with a brutal memory problem: its 2.78T parameters would occupy around 5.56 TB at full precision.

But K3 is a mixture-of-experts model.

Only 16 of its 896 experts per layer are activated for each token.

kimi-k3-in-c exploits that architecture to the extreme:
→ 93% of the model stays out of memory, read from NVMe disk only when needed
→ Weights are stored and multiplied in compressed 4-bit form
→ The dense trunk streams one layer at a time, making RAM a choice, not a limit

The whole program is just 176 KB of C code with zero external dependencies.

BEWARE it’s slow: roughly 32 seconds per token at 8 GB RAM.

You also need 1.7 TB of free disk space.

But whether you use 8 GB or 224 GB, it produces byte-identical output.

More memory just speeds up the clock.

Incredible proof that bridging the gap between a datacenter and a laptop comes down to where the bytes live.

100% free and open-source.

Repo link in 🧵↓