# Deltafin

> ARGODRIVE Deltafin: Kimi K3 (2.8T MoE) streamed from SSDs on Apple Silicon — fork of gavamedia/deltafin with the ARGODRIVE storage work and benchmark package -…

- **Website:** https://github.com/argonautlabsai/deltafin
- **Pricing:** paid
- **Categories:** Developer Tools, Research
- **Tags:** developer-tools, research, design
- **Platforms:** macOS, CLI
- **Last verified:** 2026-09-10
- **Canonical page:** https://linkrena.com/tools/deltafin

## About

TL;DR — Kimi K3 (2.8T-parameter MoE, 1.45 TB of expert weights) running on one M5 Max MacBook Pro with 128 GB, experts streamed from four SSDs.

1.00 tok/s steady decode over a 512-token answer; 1.13 over 128; 0.96 on the public 17-token prompt (upstream reported 0.68).

The honest limit: a 512-token prompt takes ~6.3 minutes to its first token. Cause found (prefill re-reads each layer's experts 8×), fix planned, not built.

Useful findings: one drive gives ≈52% of four-drive speed, two ≈73%, three ≈90% — the slowest of each layer's 16 reads sets the pace, not total bandwidth.

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