DeepSeek-V4-Flash (284B total / 13B activated) represents the most efficient large MoE model released to date — achieving 9.8× lower FLOPs and 13.7× smaller KV cache than DeepSeek-V3.2 at 1M-token context. Its activated parameter count (13B) makes it practically runnable on Apple Silicon with quantization, yet it punches well above its weight class on reasoning, code, and long-context benchmarks.
mlx-lm currently supports DeepSeek-V2 and V3 architectures. V4 introduces two new attention mechanisms — Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) — that are not yet implemented. Without native mlx-lm support, Apple Silicon users cannot run the most capable open-source MoE model available.
DeepSeek-V4-Flash (284B total / 13B activated) represents the most efficient large MoE model released to date — achieving 9.8× lower FLOPs and 13.7× smaller KV cache than DeepSeek-V3.2 at 1M-token context. Its activated parameter count (13B) makes it practically runnable on Apple Silicon with quantization, yet it punches well above its weight class on reasoning, code, and long-context benchmarks.
mlx-lm currently supports DeepSeek-V2 and V3 architectures. V4 introduces two new attention mechanisms — Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) — that are not yet implemented. Without native mlx-lm support, Apple Silicon users cannot run the most capable open-source MoE model available.