Hi GraphCast Team,
Thank you for your groundbreaking work!
I'd like to share a community project, GraphCast-Lite, designed to run GraphCast inference on consumer hardware by heavily optimizing memory and speed.
Key Highlights:
8GB VRAM Support: Runs the high-resolution 0.25° model (37 levels) in full FP32 precision on consumer GPUs (e.g., RTX 2080 Ti) using a Static VRAM Pool and Dual-Chunking Engine.
High Performance: Forecasts 10 days (40 steps) in ~15s on RTX 4090 (BF16) and ~40s on RTX 2080 Ti (FP16) via TensorRT.
Lightweight: Replaces heavy dependencies with a pure PyTorch/TensorRT inference pipeline.
Repo: <VectorElectron/graphcast-lite>
Hope this helps more researchers experiment with GraphCast without needing enterprise GPUs!
Best regards,
Hi GraphCast Team,
Thank you for your groundbreaking work!
I'd like to share a community project, GraphCast-Lite, designed to run GraphCast inference on consumer hardware by heavily optimizing memory and speed.
Key Highlights:
8GB VRAM Support: Runs the high-resolution 0.25° model (37 levels) in full FP32 precision on consumer GPUs (e.g., RTX 2080 Ti) using a Static VRAM Pool and Dual-Chunking Engine.
High Performance: Forecasts 10 days (40 steps) in ~15s on RTX 4090 (BF16) and ~40s on RTX 2080 Ti (FP16) via TensorRT.
Lightweight: Replaces heavy dependencies with a pure PyTorch/TensorRT inference pipeline.
Repo: <VectorElectron/graphcast-lite>
Hope this helps more researchers experiment with GraphCast without needing enterprise GPUs!
Best regards,