DeepGrove crams a 20B-parameter model into 5.3GB

DeepGrove published Maple-Preview, a 20-billion-parameter mixture-of-experts model that runs with just 1 billion parameters active per token, in a 5.31GB checkpoint, per its Hugging Face listing.

On a Mac mini M4, DeepGrove clocks it at 218 tokens per second. That's fast enough to run a 20B model locally without a GPU server or an API bill. That's 5 to 16 times faster than Gemma 4, Qwen3.5 and gpt-oss at comparable quality, DeepGrove reports. It backs that with scores on four benchmarks: LCBv6, AIME 2026, HMMT 2026 and GPQA-D.

How it gets there

The model has 24 layers and 256 experts, with 8 active on any given pass. Context runs to 131,072 tokens, built from a 3:1 mix of sliding-window and global attention.

The weights are ternary, which shrinks the model itself instead of paging a large one off disk. That's a different bet than the SSD-streaming approach other small-model projects use. There, a large model stays on disk and pages sections into memory as needed.

The Show HN thread backing the release passed 140 points, with one commenter reporting 120 tokens per second on an iPhone. It ships under MIT, so anyone can test the speed and quality claims independently.