TimesFM 3.0 merges native MLX backend: 641 series/sec on M4 Max, no PyTorch needed

rachittshah · x · 2026-09-08

A community PR adding an MLX backend to Google's official timesfm repo has been merged, with full feature parity with the PyTorch implementation.

• TimesFM 3.0 is Google's 330M-parameter foundation model for time-series forecasting, reading history in 32-point patches and writing the next 64 steps in a single forward pass (9 quantiles per step).
• The new backend lives in src/timesfm3/mlx with the same timesfm3forecaster interface; the torch code moved to src/timesfm3/torch with lazy re-exports, so import timesfm3 no longer pulls in PyTorch.
• M4 Max benchmarks (512-point context, 64-step horizon): 12.5 ms per series at batch 1; 641 series/sec at batch 32. Max deviation from the PyTorch reference is 2.1e-6 — float32 noise.
• The 1.9x speedup at batch 1 comes from mx.compile, not quantization: at 330M params the model is dispatch-bound, so bf16 and int8 yield little gain.

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