Self-host Voxtral Small on dedicated GPU clusters

Run Mistral's speech-to-text model on bare-metal Kubernetes in EU data centers. Your audio never leaves your infrastructure.

Top GPU offerings

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Modality
Audio in Text in Text out
Context
32k tokens (~30 minutes of audio)
80 minutes of audio
License
VRAM requirements
55 GB
Voxtral Inference
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ID: voxtral-mini-2507

Selected audio file

halting-problem.mp3

Duration

0:56

File type

MP3

360 tokens · 1% used

Language: en

Model: voxtral-mini-2507

Show segments
Alan Turing proved the halting problem is undecidable using a contradiction argument. Assume there exists a program, H, that can examine any program and input and correctly decide whether that program will eventually halt or run forever. Turing then constructs a new program, D, that uses H as a subroutine. Program D takes a program as input and does the opposite of what H predicts. If H says the program halts, D enters an infinite loop. If H says it runs forever, D halts. Now consider running D on its own code as input. If H predicts that D halts, D will loop forever. If H predicts it loops forever, D halts. In both cases, H is wrong, which contradicts the assumption that H always works. works. Therefore, no general algorithm can decide whether arbitrary programs halt.

Use Cases

Batch transcription pipelines

Queue-based, throughput-oriented transcription for steady audio streams. Files land in object storage, workers pull jobs in parallel, and structured results flow straight into search indexes, data warehouses, and CRM systems - no manual hand-off required.

Support calls
Field recordings
Internal trainings
User voice notes

Pin model version and worker pool size to your queue. Audio never crosses your network boundary.

Object Storage
Worker Pool
Voxtral Small
Transcripts + Timestamps
Search
DWH
CRM
Tickets