Great to see @MistralAI pushing real-time speech models forward with Voxtral Mini 4B Realtime.
Day 0 support is already available in @vllm_project, and Day 1 experimentation is already possible with Red Hat AI Inference Server, enabling teams to quickly test streaming ASR workloads through the new realtime audio API with sub 500ms latency and multi-language support.
At Red Hat AI, our focus is helping teams move quickly from model release to real experimentation, giving developers and platform teams an early path to test real-time transcription, voice assistants, and live captioning workloads on open infrastructure.
This is the open ecosystem moving together:
✅ Mistral releasing production-ready open models
✅ vLLM enabling immediate serving support
✅ Red Hat AI helping teams experiment on Day 1
If you are exploring real-time audio AI, this is a great place to start:
🤗 Model: https://t.co/E2atyw5mBy
📰 Announcement: https://t.co/VXNbNIQdPY
🛠️ Run with Red Hat AI Inference Server: https://t.co/dQTPC62dWw
Nemotron 3 is ready on Day 0 with Red Hat AI ✅
This quick start shows how to run NVIDIA Nemotron v3 Nano 30B-A3B immediately on the Red Hat AI Inference Server preview, including setup, BF16 or FP8 serving, and a simple curl test.
https://t.co/njaHSUPDL1
Our colleague @dougbtv built a very cool integration that shows what is possible with vLLM-Omni, a new multimodal framework introduced last week. He connected vLLM-Omni to @ComfyUI, a community favorite for designing advanced diffusion workflows.
The project includes a new endpoint in vLLM-Omni and a custom ComfyUI integration that lets diffusion models run through a clean and flexible front end. It is a great example of developers extending open source systems in practical ways.
Doug wrote a detailed walkthrough here:
https://t.co/WQeHZzKiKj
cc. @vllm_project
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