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Kimi K3 is a 2.78–2.8 trillion-parameter multimodal MoE that Pipe Network says…

2026-07-28 · 20:43 UTC ·@0xSero ·1 min read

Brief

Kimi K3 was ported to run on a Mac Studio m3 Ultra by Pipe Network (repo: PipeNetwork/kimi-k3-mlx), published 2026-07-28. They claim the original 2.78–2.8T-parameter model needed ~1.6 TB on disk; their MLX port uses a layerwise streaming converter plus REAP pruning (scoring 896 experts on a calibration corpus) to cut the footprint to ~350 GB and enable local Apple Silicon inference.

Why it matters

Kimi K3 is a 2.78–2.8 trillion-parameter multimodal MoE that Pipe Network says required ~1.6 TB on disk and was previously 'impossible to run on Apple Silicon'.

Key details

  • On 2026-07-28 Pipe Network open-sourced an MLX port (github.com/PipeNetwork/kimi-k3-mlx) that reduces the model to ~350 GB, enabling it to run on a Mac Studio m3 Ultra.
  • The port implements two key techniques: a streaming converter that walks one layer at a time so mlx_lm never materializes the full model, and REAP pruning that scores all 896 experts against a calibration corpus to keep only those needed; the repo also includes per-language expert-overlap analysis.
Source evidence

Kimi on Mac Studio m3 ultra

Pipe Network (@pipenetwork)

Run Kimi K3 on a Mac Studio 🫰

K3 is 2.8T parameters and 1.6TB on disk, which makes it impossible to run on Apple Silicon.

Until now.

Our MLX port is now open source: github.com/PipeNetwork/kimi-…

To accomplish this, we solved two things:

  1. We wrote a streaming converter that walks one layer at a time, so that mlx_lm doesn't need to materialize the whole model.

  2. REAP pruning sits on top and scores all 896 experts against a calibration corpus to keep only ones your workload needs.

That's what brings K3 down to 350GB and inside a Mac Studio.

Link

GitHub - PipeNetwork/kimi-k3-mlx: MLX port of moonshotai/Kimi-K3 (2.78T multimodal MoE): streaming...

MLX port of moonshotai/Kimi-K3 (2.78T multimodal MoE): streaming converter, REAP expert pruning, and per-language expert-overlap analysis - PipeNetwork/kimi-k3-mlx
github.com

— https://nitter.net/pipenetwork/status/2081910870083285198#m