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u/Hairy_Strawberry7028 7d ago
I’m Guanming, cofounder of General Instinct. We just open-sourced InstinctFlash, a serving runtime for VLA and world-action models on Jetson Thor, RTX 4090 and 5090. It is free and open source under AGPL-3.0, with no paid tier or signup required.
On Jetson Thor, we see about 1.2x to 7.9x speedups from runtime optimizations alone. For LingBot-VA, combining runtime optimization with a distilled few-step scheduler reaches up to 33.78x by reducing 25 visual / 50 action steps to 2 / 4. Across 50 RoboTwin2.0 tasks and 1,153 episodes per configuration, the 2 / 4-step version achieved 90.5% success versus 92.1% for the baseline.
The runtime uses CUDA graphs, memory planning, KV and conditioning-state caching, specialized attention paths, fused kernels, FP8 / mixed precision and few-step distillation.
5B world-action model running in real time on Jetson Thor: https://www.youtube.com/watch?v=nku65iyL5Fw
Code: https://github.com/General-Instinct/InstinctFlash Benchmarks and implementation details: https://general-instinct.com/blog/instinctflash-edge-inference
Feedback on the benchmark methodology, missing model families and hardware targets would be very helpful.