mlx
A 104 GB model now runs on a 48 GB Mac by streaming experts off the SSD News
slotstream, a single Swift binary released as a Show HN on September 1, 2026, runs the 104 GB Qwen3.8-Flash-Next mixture-of-experts model on Macs with a fraction of that memory by keeping a small trunk resident and reading expert weights off the SSD on demand -- about 12 tokens per second on a 48 GB machine.
The 'Ternary' 20B Model Everyone Downloaded Today Ships as a Two-Bit Package News
DeepGrove's Maple-Preview is being shared as a 20-billion-parameter ternary model that fits in about 5 gigabytes, but its native release is a 40GB BF16 repository and the small footprint belongs to a separate two-bit MLX build with no published ternary training recipe.
slotstream Tool
A single Swift binary that runs the 104 GB Qwen3.8-Flash-Next mixture-of-experts model on Apple Silicon Macs with far less memory, by streaming expert weights off the SSD. Speaks the Ollama and OpenAI chat APIs, so existing tools work unchanged. About 12 tokens per second on a 48 GB Mac; needs roughly 110 GB of free disk.
TurboQuant-MLX Tool
Quantization tooling for MLX with published size and speed measurements, including a 3-bit path that takes a 120-billion-parameter model from about 63GB down to 48GB on consumer Macs.
Maple-Preview (2-bit MLX build) Tool
DeepGrove's 20B mixture-of-experts model with about 1B active parameters per token, packaged for Apple Silicon at roughly 5.3GB. The build uses affine two-bit group quantisation with four-bit embeddings and output head, and its loader packs ternary values into two-bit codes. Note that the native BF16 repository is about 40.4GB, and DeepGrove publishes no ternary training recipe or independent evaluation.