If you’re working with large machine learning models, you know the drill: waiting for what feels like an eternity for your model to spin up. But what if I told you that those days might be behind us?
I recently stumbled upon some fascinating benchmarks that show promising results for cold start latency in large models. We’re talking:
* ~1.3 seconds for a 32B model
* ~3.7 seconds for Mixtral-141B (on A100s)
To put this into perspective, Google Cloud Run reported a whopping ~19 seconds for Gemma-3 4B earlier this year. And let’s be real, most infra teams assume 10-20 seconds (or even minutes) for 70B+ models.
But what does this mean for the future of inference? If these numbers hold up, it reframes inference as less of an ‘always-on’ requirement and more of a ‘runtime swap’ problem.
This raises some interesting questions for the community:
* How important is sub-5s cold start latency for scaling inference?
* Would it shift architectures away from dedicating GPUs per model toward more dynamic multi-model serving?
The implications are huge. Imagine being able to deploy large models without the hefty latency costs. It could change the game for inference and open up new possibilities for model serving.
What do you think? Share your thoughts in the comments below!