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Read the full article here: https://binaryverseai.com/voxtral-min... Voxtral Mini 4B is one of the first open-weights realtime speech-to-text models that actually feels like “realtime” in practice. In this video, I walk you through a clean, copy-paste local setup using vLLM’s Realtime API and the /v1/realtime WebSocket, then show the latency and VRAM knobs that stop your GPU from melting down mid-stream. You’ll learn how to run Voxtral locally with a reproducible uv virtual environment, why the download size (~9GB) has nothing to do with your runtime VRAM (yes, it can climb into the ~35GB range), and how to tune transcription delay, batching, and context so your pipeline stays fast and boringly stable. If you’re building live captions, voice agents, meeting transcription, or a privacy-first real time speech to text stack, this is the practical runbook. No fluff, just what works. What you’ll get: Exact install commands (uv + vLLM nightly + audio deps) The known-good serve config (compile cache off, cudagraph piecewise) How the vLLM realtime api and /v1/realtime websocket session actually behaves Latency tuning (the 480ms sweet spot) vs accuracy trade-offs VRAM & stability tuning to prevent OOMs A troubleshooting matrix for the common failures Chapters: 00:00 Intro: Deploying Voxtral Mini 4B 00:24 The Git Clone of Sadness 01:35 Streaming First: Causal Encoder Architecture 03:10 Hardware Reality Check: Weights vs. Runtime 04:50 The Clean Box Strategy (uv) 05:35 Installing vLLM Nightly Build 06:05 The Silent Failure Check (Audio Libs) 06:45 Serving the Model: Known-Good Config 07:45 The 5-Step Sanity Gauntlet 09:00 The Mental Model: Relationship, Not Transaction 09:50 Latency Tuning: The 480ms Sweet Spot 10:55 Stability Tuning: Preventing OOM 11:45 The Fix Kit: Troubleshooting Matrix 12:20 Production Limitations: VAD & Cocktail Party 12:55 Final Checklist: Build the Thing Subscribe for more practical AI runbooks, no hype, just working setups.