Tune the actual workload

Start with per_core_batch_size=1, a context such as 512 and a horizon rounded to 128. Record wall time and peak resident/device memory for representative lengths before increasing batch size. No table can guarantee a batch size from VRAM alone: contexts, horizon, copies, backend, compiler and concurrent work matter.

The 2.5 torch_compile loader option defaults True. Use False for first-load smokes; separately benchmark whether compilation pays off for repeated calls. model.compile(ForecastConfig(...)) is still needed with either setting. The continuous quantile head supports <=1,024 horizon. Symmetric flip averaging adds a second decode pass. Reduce context only after evaluating its accuracy cost.

For CUDA float32 matmul, torch.set_float32_matmul_precision("high") can trade internal precision for speed on supported hardware; it is not a universal accuracy-preserving setting and does not make CPU/MPS use CUDA. Use torch.cuda.get_device_properties(0).total_memory, not .total_mem.

Process many series in outer chunks if input/output arrays are large; persist outputs as you go instead of retaining every array. Passing a copied list avoids 2.5's in-place padding-list growth. gc.collect() or torch.cuda.empty_cache() does not free live model tensors or fix an oversized workload. After OOM, reduce the actual context/batch/horizon allocation and rerun the representative smoke, then reassess forecast quality.

Backend details: TimesFM torch source, PyTorch precision API.