GMI Inference Engine is a multimodal-native inference platform that runs text, image, video and audio in one unified pipeline. Get enterprise-grade scaling, observability, model versioning, and 5–6× faster inference so your multimodal apps run in real time.
Can you give us a video with less flash anmd sizzle - but actually shows how we will mak decisions om AI models, how your technology will aid this, and so on? The closest you come to this is at the end of the video, but still far from showing a solution for anything. Thanks, eager to understand, so I can use this.
Do you plan to support the latest high-performance GPU types (e.g., NVIDIA H100/A100) to cater to compute-intensive multimodal workloads? Also, will the Inference Engine integrate with popular model hubs (e.g., Hugging Face) to simplify deploying pre-trained multimodal models?
@GMI Cloud Fast multimodal native inference at scale addresses a real bottleneck for AI applications. Handling text, images, and video together without separate pipelines simplifies architecture significantly.
What sets GMI Cloud apart from other inference providers in terms of latency or cost? Are developers finding it easier to deploy multimodal models compared to traditional approaches?
Curious about how it handles model switching when workloads require different capabilities.
Nice positioning — short, clear, and aimed at serious AI teams. “From single inference nodes to multi-region AI factories” instantly shows the scale, and the “one unified dashboard” message is strong. It reads like real infrastructure, not buzzwords. If you add one outcome line (faster training, predictable costs, etc.), it becomes even more compelling.
It seems more about the creatives generation on scale, which part is about apps?
Impressive launch, GMI Cloud team. From a clarity & onboarding lens: when someone opens GMI Cloud for the first time, what’s the one belief you want them to hold in the first 10-15 seconds? Is it: • “I can build a customizable AI model without starting from scratch.” Or: • “This platform already understands my domain and scaling needs.” Because in model-infrastructure tools the biggest adoption barrier isn’t features; it’s the user believing this will really meet my business demands. Curious how you’re shaping that moment.
Been fighting GPU quotas lately—if the console hides the usual pain (SSH, firewall spaghetti), that’s a win. Curious what GPUs you’ve got on tap and how burst pricing works. Bare metal + containers in one place sounds handy, esp. for multi-region stuff.
Congrats on the launch, really strong work overall.
One quick thought that could make the page even better:
Right now the hero phrase “Build AI Without Limits” + list of offerings communicates ambition, but it’s a bit broad. Consider tightening the headline or sub-headline to clearly show the core benefit for your main user segment (for example: “Get enterprise-grade GPU access and deploy your models in minutes, no DevOps needed”)
Just to confirm, all applications run on your inference platform will be on the dedicated node, right?
Then what about pricing? Will it be much more expensive than shared platforms?
Congratulation @justin2025@louisa_guo This is exactly what AI teams need - fast deployment without fighting infrastructure. Love the unified console approach, huge time saver.
You mentioned "5–6× faster inference" in your description, so what is this being compared to?
Okay a lot of informations on the website. But where is it hosted? Is it wrapped around Goolge Cloud, AWS or do your have your own datacenters with the GPUs. I don't really get it.
Can you give us a video with less flash anmd sizzle - but actually shows how we will mak decisions om AI models, how your technology will aid this, and so on? The closest you come to this is at the end of the video, but still far from showing a solution for anything. Thanks, eager to understand, so I can use this.