AI/ML
Deploy Voxtral on AWS EC2 with Hugging Face: Step by Step with GPU Support
Why Deploy Voxtral on AWS EC2?
Deploying Mistral Voxtral on AWS EC2 with GPU support allows you to run real-time speech-to-text transcription and voice understanding AI at scale. EC2 provides scalable compute power while Hugging Face simplifies model access and inference. Perfect for 1. Real-time transcription apps 2. LLM voice input pipelines 3. Scalable AI APIs 4. Voice automation tools EC2 + GPU: Prerequisites 1. AWS account with EC2 launch permissions 2. Preferred GPU instance (e.g., g4dn.xlarge, g5.xlarge, p3.2xlarge) 3. Ubuntu 20.04 or 22.04 base image 4. Security group with ports 22 (SSH) and 5000 (API) open 5. SSH key pair for secure login Step by Step Guide to Deploy Voxtral on AWS EC2
Step 1: Launch GPU Instance
1. Go to AWS EC2 Dashboard → Launch Instance
2. Choose Ubuntu AMI (20.04 or 22.04)
3. Select GPU instance type (e.g., g4dn.xlarge)
4. Add storage (min 50GB)
5. Create or select a key pair
6. Open port 22 (SSH) and optionally 5000 for API access
Step 2: Connect via SSH
ssh -i ~/.ssh/your-key.pem ubuntu@your-ec2-public-ip
Step 3: Install NVIDIA Drivers & Docker (Optional)
sudo apt update && sudo apt upgrade -ysudo apt install -y build-essential git curl wget unzip ffmpegwget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-repo-ubuntu2004_11.8.0-1_amd64.debsudo dpkg -i cuda-repo-ubuntu2004_11.8.0-1_amd64.debsudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pubsudo apt update && sudo apt install -y cudaStep 4: Set Up Python Environment
sudo apt install -y python3-pippip3 install --upgrade pippip3 install torch torchaudio transformers accelerateStep 5: Load Voxtral via Hugging Face Transformers
from transformers import pipelinetranscriber = pipeline("automatic-speech-recognition", model="mistral-community/voxtral-base")result = transcriber("sample.wav")print(result['text'])
Voxtral on Hugging Face: https://huggingface.co/mistral-community
Step 6 (Optional): Expose as API with FastAPI
pip install fastapi uvicorn
from fastapi import FastAPI, UploadFile
app = FastAPI()@app.post("/transcribe")async def transcribe(file: UploadFile): audio = await file.read() with open("temp.wav", "wb") as f: f.write(audio) result = transcriber("temp.wav") return {"text": result['text']}
Run server:
uvicorn filename:app --host 0.0.0.0 --port 5000Optimization Tips
- Use g5 instances for better price-performance
- Store large audio files in S3 and process via Lambda trigger
- Use torch.compile() or onnxruntime for model speedups
- Enable HTTPS for public API via Nginx + SSL
Overall Winner (Open Ecosystem): Voxtral by Mistral
Running Voxtral on AWS EC2 with GPU acceleration allows developers and startups to build robust, scalable and real-time speech AI applications. Whether you’re developing voice assistants, transcription APIs or integrating audio pipelines into LLMs Voxtral is production ready and open source.
Need help with auto scaling, enterprise setup or S3 integration? Contact us for a tailored implementation.
AI/ML