50+ LLMs on 2 GPUs with 2-Second Swapping? We built AI-Native Runtime
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
50+ LLMs on 2 GPUs with 2-Second Swapping? We built AI-Native Runtime launched on May 16, 2025 with 5 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
InferX: Inference as a Service Platform. Contribute to inferx-net/inferx development by creating an account on GitHub.
As the Step#1 and #2 won't take any GPU resource, InferX will "pre-warm" the container before the user request comming, i.e. start the container to finish Step#1 and Step#2 and blocks at before Step#3. Per our test, the Step#1 and #2 will take around 1-3 seconds, the optimization will save the 1-3 seconds from CS-TTFT. The Standby Container will takes about 200-400MB CPUfrom github.com
In the maker’s words, at launch
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its…
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