Runpod让部署大模型像serverless函数一样快，冷启动200ms，成本降90%，不用再纠结闲置GPU还是等待加载了。
Runpod推出针对AI模型的serverless服务，解决大模型加载慢和空闲GPU成本高的问题。他们采用FlashBoot技术，将空闲模型转移到廉价存储，流量恢复时快速调回GPU，冷启动时间低于200ms。部署时弃用Docker，改用Python函数，指定GPU后几秒即可完成。该方案声称比主流云厂商降低成本90%。
Serveless, but for models, is here! I stopped ren…
Serveless, but for models, is here!
I stopped renting servers 12 years ago. I mostly moved everything to serverless functions.
It's taken a long time for this idea to work with AI models.
The issue was simple: You can start a simple function in milliseconds and scale it down to zero for free, but loading a 20GB model into memory takes quite a bit of time.
And once the model was loaded, forget about shutting it down. You'd better pay for an idle GPU than force the next user to wait while everything reloads.
So you have two options:
1. Optimize for low-latency answers and pay for an idle GPU, or 2. Optimize for lower costs by scaling to zero and making users wait
Both are bad, and fortunately, we don't need to choose anymore.
Runpod, the new AI developer cloud, is doing something really clever to solve this problem:
• They don't shut down idle models • Instead, they move them to somewhere cheaper • When traffic returns, they page the model back to the GPU
This whole process is extremely fast: cold starts drop below 200ms for busy endpoints. They call this technique "FlashBoot".
On top of that, to deploy your models, they ditched Docker because they don't want you spending time rebuilding a massive image every time you change a line. Instead, you get a Python function with a line for the GPU you need. You can deploy in seconds with this.
The first run is always the slowest because you're installing dependencies, but after that, you can run very large models very quickly.
In summary:
1. Serverless is here for models 2. You don't need a better GPU 3. It's actually 90% cheaper than 90% of what big clouds give you